Machine Learning
Webpage: http://www.cs.utexas.edu/users/ml/
Director: Raymond Mooney
Machine learning is the study of adaptive computational systems that improve performance with experience. The Machine Learning research group at UT Austin is led by Raymond J. Mooney, and our research focuses on combining empirical and knowledge-based learning techniques, including applications such as natural language acquisition, knowledge refinement, learning for planning, and recommender systems.
People
Vanya Cohen Ph.D. Student vanya [at] utexas edu
Adeline Foote Undergraduate Student addiefoote [at] utexas edu
Jierui Li Ph.D. Student jierui [at] cs utexas edu
Raymond J. Mooney Faculty mooney [at] cs utexas edu
Jordan Voas Ph.D. Student jvoas [at] utexas edu
Albert Yu Ph.D. Student albertyu [at] utexas edu
Publications
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A Survey of Robotic Language Grounding: Tradeoffs Between Symbols and Embeddings 2024
Vanya Cohen, Jason Xinyu Liu, Raymond Mooney, Stefanie Tellex, David Watkins, International Joint Conference on Artificial Intelligence (IJCAI) (2024).
CAPE: Corrective Actions from Precondition Errors using Large Language Models 2024
Shreyas Sundara Raman, Vanya Cohen, Ifrah Idrees, Eric Rosen, Raymond Mooney, Stefanie Tellex, and David Paulius, International Conference on Robotics and Automation (ICRA) (2024).
CAT-BENCH: Benchmarking Language Model Understanding of Causal and Temporal Dependencies in Plans 2024
Yash Kumar Lal, Vanya Cohen, Nathanael Chambers, Niranjan Balasubramanian, Raymond Mooney, Empirical Methods in Natural Language Processing (EMNLP) (2024).
CONTRADOC: Understanding Self-Contradictions in Documents with Large Language Models 2024
Jierui Li, Vipul Raheja, Dhruv Kumar, North American Chapter of the Association for Computational Linguistics (NAACL) (2024).
Distilling Algorithmic Reasoning from LLMs via Explaining Solution Programs 2024
Jierui Li and Raymond Mooney, preprint (2024).
Measuring Sound Symbolism in Audio-visual Models 2024
Wei-Cheng Tseng, Yi-Jen Shih, David Harwath, Raymond Mooney, IEEE Spoken Language Technology (SLT) Workshop (2024).
Multimodal Contextualized Semantic Parsing from Speech 2024
Jordan Voas, Raymond Mooney, David Harwath, Association for Computational Linguistics (ACL) (2024).
Natural Language Can Help Bridge the Sim2Real Gap 2024
Albert Yu, Adeline Foote, Raymond Mooney, and Roberto Martín-Martín, Robotics, Science and Systems (RSS) (2024).
Sparse Meets Dense: A Hybrid Approach to Enhance Scientific Document Retrieval 2024
Priyanka Mandikal, Raymond Mooney, The 4th Workshop on Scientific Document Understanding, AAAI (2024).
When is Tree Search Useful for LLM Planning? It Depends on the Discriminator 2024
Ziru Chen, Michael White, Raymond Mooney, Ali Payani, Yu Su, Huan Sun, Association for Computational Linguistics (ACL) (2024).
“Female Astronaut: Because sandwiches won’t make themselves up there!": Towards multi-modal misogyny detection in memes 2023
Smriti Singh, Amritha Haridasan, Raymond Mooney, Association of Computational Linguistics (ACL), Workshop on Online Abuse and Harms (WOAH) (2023).
Directly Optimizing Evaluation Metrics to Improve Text to Motion 2023
Yili Wang, Masters Thesis, Department of Computer Science, UT Austin.
Explaining Competitive-Level Programming Solutions using LLMs 2023
Jierui Li, Szymon Tworkowski, Yingying Wu, Raymond Mooney, Association of Computational Linguistics (ACL), Natural Language Reasoning and Structured Explanations Workshop (2023).
Learning Deep Semantics for Test Completion 2023
Pengyu Nie, Rahul Banerjee, Junyi Jessy Li, Raymond Mooney and Milos Gligoric, International Conference on Software Engineering (2023).
SAGEViz: SchemA GEneration and Visualization 2023
Sugam Devare, Mahnaz Koupaee, Gautham Gunapati, Sayontan Ghosh, Sai Vallurupalli, Yash Kumar Lal, Francis Ferraro, Nathanael Chambers, Greg Durrett, Raymond Mooney, Katrin Erk, Niranjan Balasubramanian, Empirical Methods in Natural Language Processing (EMNLP) Demo Track (2023).
Text-to-SQL Error Correction with Language Models of Code 2023
Ziru Chen, Shijie Chen, Michael White, Raymond Mooney, Ali Payani, Jayanth Srinivasa, Yu Su, Huan Sun, In Proceedings of the Association for Computational Linguistics (ACL), January 2023.
Using Both Demonstrations and Language Instructions to Efficiently Learn Robotic Tasks 2023
Albert Yu, Raymond J. Mooney, International Conference on Learning Representations (2023).
Using Planning to Improve Semantic Parsing of Instructional Texts 2023
Vanya Cohen, Raymond Mooney, Association of Computational Linguistics (ACL), Natural Language Reasoning and Structured Explanations Workshop (2023).
What is the Best Automated Metric for Text to Motion Generation? 2023
Jordan Voas, Masters Thesis, Department of Computer Science, UT Austin.
What is the Best Automated Metric for Text to Motion Generation? 2023
Jordan Voas, Yili Wang, Qixing Huang, Raymond Mooney, In ACM SIGGRAPH Asia, December 2023.
End-to-End Learning to Follow Language Instructions with Compositional Policies 2022
Vanya Cohen, Geraud Nangue Tasse, Nakul Gopalan, Steven James, Ray Mooney, Benjamin Rosman, Workshop on Language and Robot Learning at CoRL 2022 (2022).
Entity-Focused Dense Passage Retrieval for Outside-Knowledge Visual Question Answering 2022
Jialin Wu, Raymond Mooney, In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP), December 2022.
Facilitating Software Evolution through Natural Language Comments and Dialogue 2022
Sheena Panthaplackel, PhD Thesis, Department of Computer Science, UT Austin.
Impact of Evaluation Methodologies on Code Summarization 2022
Pengyu Nie, Jiyang Zhang, Junyi Jessy Li, Raymond J. Mooney, and Milos Gligoric, In Annual Meeting of the Association for Computational Linguistics, May 2022.
Incorporating External Information for Visual Question Answering 2022
Jialin Wu, PhD Thesis, Department of Computer Science, UT Austin.
Learning to Describe Solutions for Bug Reports Based on Developer Discussions 2022
Sheena Panthaplackel, Junyi Jessy Li, Milos Gligoric, Raymond J. Mooney, In Findings of the Annual Meeting of the Association for Computational Linguistics (ACL), May 2022.
Multi-Modal Answer Validation for Knowledge-Based VQA 2022
Jialin Wu, Jiasen Lu, Ashish Sabharwal, Roozbeh Mottaghi, Proceedings of the AAAI Conference on Artificial Intelligence (2022).
Planning with Large Language Models via Corrective Re-prompting 2022
Shreyas Sundara Raman, Vanya Cohen, Eric Rosen, Ifrah Idrees, David Paulius, Stefanie Tellex, Foundation Models for Decision Making Workshop at NeurIPS 2022 (2022).
Towards Automated Error Analysis: Learning to Characterize Errors 2022
Tong Gao, Shivang Singh, Raymond J. Mooney, Short version appears in the 19th International Florida Artificial Intelligence Research Society Conference (FLAIRS) (2022).
Updated Headline Generation: Creating Updated Summaries for Evolving News Stories 2022
Sheena Panthaplackel, Adrian Benton, Mark Dredze, In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, May 2022.
Using Commonsense Knowledge to Answer Why-Questions 2022
Yash Kumar Lal, Niket Tandon, Tanvi Aggarwal, Horace Liu, Nathanael Chambers, Raymond Mooney, Niranjan Balasubramanian, In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, December 2022.
Using Developer Discussions to Guide Fixing Bugs in Software 2022
Sheena Panthaplackel, Milos Gligoric, Junyi Jessy Li, Raymond J. Mooney, In Findings of the 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP), December 2022.
Using Natural Language to Aid Task Specification in Sequential Decision Making Problems 2022
Prasoon Goyal, PhD Thesis, Department of Computer Science, UT Austin.
Zero-shot Video Moment Retrieval With Off-the-Shelf Models 2022
Anuj Diwan, Puyuan Peng, Raymond J. Mooney, In Workshop on Transfer Learning for Natural Language Processing at NeurIPS 2022, December 2022.
A Recap of Early Work onTheory and Knowledge Refinement 2021
Raymond J. Mooney, Jude W. Shavlik, In AAAI Spring Symposium on Combining Machine Learning and Knowledge Engineering, March 2021.
Copy That! Editing Sequences by Copying Spans 2021
Sheena Panthaplackel, Miltiadis Allamanis, Marc Brockschmidt, In The AAAI Conference on Artificial Intelligence (AAAI), February 2021.
Deep Just-In-Time Inconsistency Detection Between Comments and Source Code 2021
Sheena Panthaplackel, Junyi Jessy Li, Milos Gligoric, Raymond J. Mooney, In The AAAI Conference on Artificial Intelligence (AAAI), Vol. arXiv:2010.01625, February 2021.
Dialog Policy Learning for Joint Clarification and Active Learning Queries 2021
Aishwarya Padmakumar, Raymond J. Mooney, In The AAAI Conference on Artificial Intelligence (AAAI), Vol. , February 2021.
Facilitating Software Evolution through Natural Language Comments and Dialogue 2021
Sheena Panthaplackel, Ph.D. Proposal.
Improving VQA and its Explanations by Comparing Competing Explanations 2021
Jialin Wu, Liyan Chen, Raymond J. Mooney, In The AAAI Conference on Artificial Intelligence (AAAI), Explainable Agency in Artificial Intelligence Workshop, Vol. arXiv:2006.15631, February 2021.
Incorporating Textual Resources to Improve Visual Question Answering 2021
Jialin Wu, Ph.D. Proposal.
Supervised Attention from Natural Language Feedback for Reinforcement Learning 2021
Clara Cecilia Cannon, Masters Thesis, Department of Computer Science, The University of Texas at Austin.
TellMeWhy: A Dataset for Answering Why-Questions in Narratives 2021
Yash Kumar Lal, Nathanael Chambers, Raymond Mooney, Niranjan Balasubramanian, In Findings of ACL 2021, August 2021.
Using Natural Language to Aid Task Specification in Sequential Decision Making Problems 2021
Prasoon Goyal, Ph.D. Proposal.
Zero-shot Task Adaptation using Natural Language 2021
Prasoon Goyal, Raymond J. Mooney, Scott Niekum, Arxiv (2021).
Associating Natural Language Comment and Source Code Entities 2020
Sheena Panthaplackel, Milos Gligoric, Raymond J. Mooney and Junyi Jessy Li, In The AAAI Conference on Artificial Intelligence (AAAI), February 2020.
Dialog as a Vehicle for Lifelong Learning 2020
Aishwarya Padmakumar, Raymond J. Mooney, In Position Paper Track at the SIGDIAL Special Session on Physically Situated Dialogue (RoboDial 2.0), July 2020.
Dialog as a Vehicle for Lifelong Learning of Grounded Language Understanding Systems 2020
Aishwarya Padmakumar, PhD Thesis, Department of Computer Science, The University of Texas at Austin.
Evaluating the Robustness of Natural Language Reward Shaping Models to Spatial Relations 2020
Antony Yun, Undergraduate Honors Thesis, Computer Science Department, University of Texas at Austin.
Jointly Improving Parsing and Perception for Natural Language Commands through Human-Robot Dialog 2020
Jesse Thomason, Aishwarya Padmakumar, Jivko Sinapov, Nick Walker, Yuqian Jiang, Harel Yedidsion, Justin Hart, Peter Stone, Raymond J. Mooney, The Journal of Artificial Intelligence Research (JAIR), Vol. 67 (2020), pp. 327-374.
Learning to Update Natural Language Comments Based on Code Changes 2020
Sheena Panthaplackel, Pengyu Nie, Milos Gligoric, Junyi Jessy Li, and Raymond J. Mooney, In Proceedings of the 58th Annual Conference of the Association for Computational Linguistics (ACL), July 2020.
PixL2R: Guiding Reinforcement Learning using Natural Language by Mapping Pixels to Rewards 2020
Prasoon Goyal, Scott Niekum, Raymond J. Mooney, In 4th Conference on Robot Learning (CoRL), November 2020. Also presented on the 1st Language in Reinforcement Learning (LaReL) Workshop at ICML, July 2020 (Best Paper Award), the 6th Deep Rein...
Systematic Generalization on gSCAN with Language Conditioned Embedding 2020
Tong Gao, Qi Huang and Raymond J. Mooney, In The 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing , December 2020.
A Framework for Writing Trigger - Action Todo Comments in Executable Format 2019
Pengyu Nie, Rishabh Rai, Junyi Jessy Li, Sarfraz Khurshid, Raymond J. Mooney, Milos Gligoric, In Proceedings of the ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE), Tallinn, Estonia, August 2019. Distinguished Paper ...
AInix: An open platform for natural language interfaces to shell commands 2019
David Gros, Undergraduate Honors Thesis, Computer Science Department, University of Texas at Austin.
Do Human Rationales Improve Machine Explanations? 2019
Julia Strout, Ye Zhang, Raymond J. Mooney, In Proceedings of the Second BlackboxNLP Workshop at ACL, pp. 56-62, Florence, Italy, August 2019.
Faithful Multimodal Explanation for Visual Question Answering 2019
Jialin Wu and Raymond J. Mooney, In Proceedings of the Second BlackboxNLP Workshop at ACL, pp. 103-112, Florence, Italy, August 2019.
Generating Question Relevant Captions to Aid Visual Question Answering 2019
Jialin Wu, Zeyuan Hu, Raymond J. Mooney, In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL), Florence, Italy, August 2019.
Hidden State Guidance: Improving Image Captioning using An Image Conditioned Autoencoder 2019
Jialin Wu and Raymond J. Mooney, In Proceedings of the Visually Grounded Interaction and Language Workshop at NeurIPS 2019, December 2019.
Improving Grounded Natural Language Understanding through Human-Robot Dialog 2019
Jesse Thomason, Aishwarya Padmakumar, Jivko Sinapov, Nick Walker, Yuqian Jiang, Harel Yedidsion, Justin Hart, Peter Stone, and Raymond J. Mooney, In IEEE International Conference on Robotics and Automation (ICRA), Montreal, Canada, May 2019.
Optimal Use Of Verbal Instructions For Multi-Robot Human Navigation Guidance 2019
Harel Yedidsion, Jacqueline Deans, Connor Sheehan, Mahathi Chillara, Justin Hart, Peter Stone, and Raymond J. Mooney, In Proceedings of the Eleventh International Conference on Social Robotics, pp. 133-143 2019. Springer.
Self-Critical Reasoning for Robust Visual Question Answering 2019
Jialin Wu and Raymond J. Mooney, In Proceedings of Neural Information Processing Systems (NeurIPS) , December 2019.
Using Natural Language for Reward Shaping in Reinforcement Learning 2019
Prasoon Goyal, Scott Niekum, Raymond J. Mooney, In Proceedings of the 28th International Joint Conference on Artificial Intelligence, Macao, China, August 2019.
Continually Improving Grounded Natural Language Understanding through Human-Robot Dialog 2018
Jesse Thomason, PhD Thesis, Department of Computer Science, The University of Texas at Austin.
Explainable Improved Ensembling for Natural Language and Vision 2018
Nazneen Rajani, PhD Thesis, Department of Computer Science, The University of Texas at Austin.
Generating Animated Videos of Human Activities from Natural Language Descriptions 2018
Angela S. Lin, Lemeng Wu, Rodolfo Corona , Kevin Tai , Qixing Huang , Raymond J. Mooney, In Proceedings of the Visually Grounded Interaction and Language Workshop at NeurIPS 2018, December 2018.
Guiding Exploratory Behaviors for Multi-Modal Grounding of Linguistic Descriptions 2018
Jesse Thomason, Jivko Sinapov, Raymond Mooney, Peter Stone, In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18) , February 2018.
Improved Models and Queries for Grounded Human-Robot Dialog 2018
Aishwarya Padmakumar, PhD Proposal, Department of Computer Science, The University of Texas At Austin.
Interaction and Autonomy in RoboCup@Home and Building-Wide Intelligence 2018
Justin Hart, Harel Yedidsion, Yuqian Jiang, Nick Walker, Rishi Shah, Jesse Thomason, Aishwarya Padmakumar, Rolando Fernandez, Jivko Sinapov, Raymond Mooney, Peter Stone, In Artificial Intelligence (AI) for Human-Robot Interaction (HRI) symposium, AAAI Fall Symposium Series, Arlington, Virginia, October 2018.
Joint Image Captioning and Question Answering 2018
Jialin Wu, Zeyuan Hu and Raymond J. Mooney , In VQA Challenge and Visual Dialog Workshop at the 31st IEEE Conference on Computer Vision and Pattern Recognition (CVPR-18) , June 2018.
Jointly Improving Parsing and Perception for Natural Language Commands through Human-Robot Dialog 2018
Jesse Thomason, Aishwarya Padmakumar, Jivko Sinapov, Nick Walker, Yuqian Jiang, Harel Yedidsion, Justin Hart, Peter Stone, and Raymond J. Mooney, In RSS Workshop on Models and Representations for Natural Human-Robot Communication (MRHRC-18). Robotics: Science and Systems (RSS), June 2018.
Jointly Improving Parsing and Perception for Natural Language Commands through Human-Robot Dialog 2018
Jesse Thomason, Aishwarya Padmakumar, Jivko Sinapov, Nick Walker, Yuqian Jiang, Harel Yedidsion, Justin Hart, Peter Stone, and Raymond J. Mooney, In Late-breaking Track at the SIGDIAL Special Session on Physically Situated Dialogue (RoboDIAL-18), Melbourne, Australia, July 2018.
Learning a Policy for Opportunistic Active Learning 2018
Aishwarya Padmakumar, Peter Stone, Raymond J. Mooney, In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP-18), Brussels, Belgium, November 2018.
Multi-modal Predicate Identification using Dynamically Learned Robot Controllers 2018
Saeid Amiri, Suhua Wei, Shiqi Zhang, Jivko Sinapov, Jesse Thomason, and Peter Stone, In Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI-18), Stockholm, Sweden, July 2018.
Natural Language Processing and Program Analysis for Supporting Todo Comments as Software Evolves 2018
Pengyu Nie, Junyi Jessy Li, Sarfraz Khurshid, Raymond Mooney, Milos Gligoric, In In Proceedings of the AAAI Workshop on NLP for Software Engineering, February 2018.
Stacking With Auxiliary Features for Visual Question Answering 2018
Nazneen Fatema Rajani, Raymond J. Mooney, In Proceedings of the 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 2217-2226 2018.
Advances in Statistical Script Learning 2017
Karl Pichotta, PhD Thesis, Department of Computer Science, The University of Texas at Austin.
BWIBots: A platform for bridging the gap between AI and human--robot interaction research 2017
Piyush Khandelwal, Shiqi Zhang, Jivko Sinapov, Matteo Leonetti, Jesse Thomason, Fangkai Yang, Ilaria Gori, Maxwell Svetlik, Priyanka Khante, Vladimir Lifschitz, J. K. Aggarwal, Raymond Mooney, and Peter Stone, The International Journal of Robotics Research (2017).
Captioning Images with Diverse Objects 2017
Subhashini Venugopalan, Lisa Anne Hendricks, Marcus Rohrbach, Raymond Mooney, Trevor Darrell, and Kate Saenko, In Proceedings of the 30th IEEE Conference on Computer Vision and Pattern Recognition (CVPR-17), pp. 5753--5761 2017.
Dialog for Language to Code 2017
Shobhit Chaurasia and Raymond J. Mooney, In Proceedings of the 8th International Joint Conference on Natural Language Processing (IJCNLP-17), pp. 175-180, Taipei, Taiwan, November 2017.
Dialog for Natural Language to Code 2017
Shobhit Chaurasia, Masters Thesis, Computer Science Department, University of Texas at Austin.
Distributional modeling on a diet: One-shot word learning from text only 2017
Su Wang, Stephen Roller, and Katrin Erk, In In Proceedings of the 8th International Joint Conference on Natural Language Processing (IJCNLP-17), Taipei, Taiwan, November 2017.
Ensembling Visual Explanations for VQA 2017
Nazneen Fatema Rajani, Raymond J. Mooney, In Proceedings of the NIPS 2017 workshop on Visually-Grounded Interaction and Language (ViGIL), December 2017.
Guiding Interaction Behaviors for Multi-modal Grounded Language Learning 2017
Jesse Thomason, Jivko Sinapov, and Raymond J. Mooney, In Proceedings of the Workshop on Language Grounding for Robotics at ACL 2017 (RoboNLP-17), Vancouver, Canada, August 2017.
Improving Black-box Speech Recognition using Semantic Parsing 2017
Rodolfo Corona, Jesse Thomason, and Raymond J. Mooney, In Proceedings of the 8th International Joint Conference on Natural Language Processing (IJCNLP-17), pp. 122-127, Taipei, Taiwan, November 2017.
Integrated Learning of Dialog Strategies and Semantic Parsing 2017
Aishwarya Padmakumar, Jesse Thomason, and Raymond J. Mooney, In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2017), pp. 547--557, Valencia, Spain, April 2017.
Leveraging Discourse Information Effectively for Authorship Attribution 2017
Elisa Ferracane, Su Wang, and Raymond J. Mooney, In In Proceedings of the 8th International Joint Conference on Natural Language Processing (IJCNLP-17), pp. 584–593, Taipei, Taiwan, November 2017.
Multi-Modal Word Synset Induction 2017
Jesse Thomason and Raymond J. Mooney, In Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI-17), pp. 4116--4122, Melbourne, Australia 2017.
Natural-Language Video Description with Deep Recurrent Neural Networks 2017
Subhashini Venugopalan, PhD Thesis, Department of Computer Science, The University of Texas at Austin.
Opportunistic Active Learning for Grounding Natural Language Descriptions 2017
Jesse Thomason, Aishwarya Padmakumar, Jivko Sinapov, Justin Hart, Peter Stone, and Raymond J. Mooney, In Proceedings of the 1st Annual Conference on Robot Learning (CoRL-17), Sergey Levine and Vincent Vanhoucke and Ken Goldberg (Eds.), pp. 67--76, Mountain View, California, November 2017. PMLR.
Stacking With Auxiliary Features 2017
Nazneen Fatema Rajani and Raymond J. Mooney, In Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI-17), pp. 2634-2640, Melbourne, Australia 2017.
Using Explanations to Improve Ensembling of Visual Question Answering Systems 2017
Nazneen Fatema Rajani and Raymond J. Mooney, In Proceedings of the IJCAI 2017 Workshop on Explainable Artificial Intelligence (XAI), pp. 43-47, Melbourne, Australia, August 2017.
An Analysis of Using Semantic Parsing for Speech Recognition 2016
Rodolfo Corona, Undergraduate Honors Thesis, Computer Science Department, University of Texas at Austin.
Combining Supervised and Unsupervised Ensembles for Knowledge Base Population 2016
Nazneen Fatema Rajani and Raymond J. Mooney, To Appear In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing (EMNLP-16) 2016.
Continuously Improving Natural Language Understanding for Robotic Systems through Semantic Parsing, Dialog, and Multi-modal Perception 2016
Jesse Thomason, PhD proposal, Department of Computer Science, The University of Texas at Austin.
Deep Compositional Captioning: Describing Novel Object Categories without Paired Training Data 2016
Lisa Anne Hendricks, Subhashini Venugopalan, Marcus Rohrbach, Raymond Mooney, Kate Saenko, and Trevor Darrell, In Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR-16), pp. 1--10 2016.
Improved Semantic Parsers For If-Then Statements 2016
I. Beltagy and Chris Quirk, To Appear In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (ACL-16), Berlin, Germany 2016.
Improving LSTM-based Video Description with Linguistic Knowledge Mined from Text 2016
Subhashini Venugopalan, Lisa Anne Hendricks, Raymond Mooney, and Kate Saenko, In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing (EMNLP-16), pp. 1961--1966, Austin, Texas 2016.
Learning Multi-Modal Grounded Linguistic Semantics by Playing "I Spy" 2016
Jesse Thomason, Jivko Sinapov, Maxwell Svetlik, Peter Stone, and Raymond J. Mooney, In Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI-16), pp. 3477--3483, New York City 2016.
Learning Statistical Scripts with LSTM Recurrent Neural Networks 2016
Karl Pichotta and Raymond J. Mooney, In Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI-16), Phoenix, Arizona, February 2016.
MGNC-CNN: A Simple Approach to Exploiting Multiple Word Embeddings for Sentence Classification 2016
Ye Zhang, Stephen Roller, and Byron Wallace., In Proceedings of the 15th Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-16), pp. 1522--1527, San Diego, California 2016.
Natural Language Semantics Using Probabilistic Logic 2016
I. Beltagy, PhD Thesis, Department of Computer Science, The University of Texas at Austin.
PIC a Different Word: A Simple Model for Lexical Substitution in Context 2016
Stephen Roller and Katrin Erk, In Proceedings of the 15th Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-16), pp. 1121-1126, San Diego, California 2016.
Representing Meaning with a Combination of Logical and Distributional Models 2016
I. Beltagy, Stephen Roller, Pengxiang Cheng, Katrin Erk, and Raymond J. Mooney, The special issue of Computational Linguistics on Formal Distributional Semantics, Vol. 42, 4 (2016).
Stacking With Auxiliary Features 2016
Nazneen Fatema Rajani and Raymond J. Mooney, ArXiv preprint arXiv:1605.08764 (2016).
Stacking With Auxiliary Features for Combining Supervised and Unsupervised Ensembles 2016
Nazneen Fatema Rajani and Raymond J. Mooney, In Proceedings of the Ninth Text Analysis Conference (TAC 2016) 2016.
Stacking With Auxiliary Features: Improved Ensembling for Natural Language and Vision 2016
Nazneen Fatema Rajani, PhD proposal, Department of Computer Science, The University of Texas at Austin.
Statistical Script Learning with Recurrent Neural Networks 2016
Karl Pichotta and Raymond J. Mooney, In Proceedings of the Workshop on Uphill Battles in Language Processing (UBLP) at EMNLP 2016, Austin, TX, November 2016.
Using Sentence-Level LSTM Language Models for Script Inference 2016
Karl Pichotta and Raymond J. Mooney, In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (ACL-16), pp. 279--289, Berlin, Germany 2016.
A Supertag-Context Model for Weakly-Supervised CCG Parser Learning 2015
Dan Garrette, Chris Dyer, Jason Baldridge, and Noah A. Smith , In Proceedings of the 2015 Conference on Computational Natural Language Learning (CoNLL-2015), pp. 22--31, Beijing, China 2015.
Inducing Grammars from Linguistic Universals and Realistic Amounts of Supervision 2015
Dan Garrette, PhD Thesis, Department of Computer Science, The University of Texas at Austin.
Knowledge Base Population using Stacked Ensembles of Information Extractors 2015
Vidhoon Viswanathan, Masters Thesis, Department of Computer Science, The University of Texas at Austin.
Knowledge Transfer Using Latent Variable Models 2015
Ayan Acharya, PhD Thesis, Department of Electrical and Computer Engineering, The University of Texas at Austin.
Language to Code: Learning Semantic Parsers for If-This-Then-That Recipes 2015
Chris Quirk, Raymond Mooney, and Michel Galley, In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics (ACL-15), pp. 878--888, Beijing, China, July 2015.
Learning to Interpret Natural Language Commands through Human-Robot Dialog 2015
Jesse Thomason, Shiqi Zhang, Raymond Mooney, and Peter Stone, In Proceedings of the 2015 International Joint Conference on Artificial Intelligence (IJCAI), pp. 1923--1929, Buenos Aires, Argentina, July 2015.
Natural Language Video Description using Deep Recurrent Neural Networks 2015
Subhashini Venugopalan, PhD proposal, Department of Computer Science, The University of Texas at Austin.
On the Proper Treatment of Quantifiers in Probabilistic Logic Semantics 2015
I. Beltagy and Katrin Erk, In Proceedings of the 11th International Conference on Computational Semantics (IWCS-2015), London, UK, April 2015.
Sequence to Sequence -- Video to Text 2015
Subhashini Venugopalan, Marcus Rohrbach, Jeff Donahue, Raymond J. Mooney, Trevor Darrell, and Kate Saenko, In Proceedings of the 2015 International Conference on Computer Vision (ICCV-15), Santiago, Chile, December 2015.
Stacked Ensembles of Information Extractors for Knowledge-Base Population 2015
Vidhoon Viswanathan, Nazneen Fatema Rajani, Yinon Bentor, and Raymond J. Mooney, In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics (ACL-15), pp. 177-187, Beijing, China, July 2015.
Stacked Ensembles of Information Extractors for Knowledge-Base Population by Combining Supervised and Unsupervised Approaches 2015
Nazneen Fatema Rajani and Raymond J Mooney, In Proceedings of the Eighth Text Analysis Conference (TAC 2015), November 2015.
Statistical Script Learning with Recurrent Neural Nets 2015
Karl Pichotta, PhD proposal, Department of Computer Science, The University of Texas at Austin.
Translating Videos to Natural Language Using Deep Recurrent Neural Networks 2015
Subhashini Venugopalan, Huijuan Xu, Jeff Donahue, Marcus Rohrbach, Raymond Mooney, and Kate Saenko, In Proceedings the 2015 Conference of the North American Chapter of the Association for Computational Linguistics -- Human Language Technologies (NAACL HLT 2015), pp. 1494--1504, Denver, Colora...
Unsupervised Code-Switching for Multilingual Historical Document Transcription 2015
Dan Garrette, Hannah Alpert-Abrams, Taylor Berg-Kirkpatrick, and Dan Klein , In Proceedings the 2015 Conference of the North American Chapter of the Association for Computational Linguistics -- Human Language Technologies (NAACL HLT 2015), pp. 1036--1041, Denver, Colora...
Weakly-Supervised Grammar-Informed Bayesian CCG Parser Learning 2015
Dan Garrette, Chris Dyer, Jason Baldridge, Noah A. Smith, In Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI-15), Austin, TX, January 2015.
Active Multitask Learning Using Both Latent and Supervised Shared Topics 2014
Ayan Acharya, Raymond J. Mooney, and Joydeep Ghosh, In Proceedings of the 2014 SIAM International Conference on Data Mining (SDM14), Philadelphia, Pennsylvania, April 2014.
Efficient Markov Logic Inference for Natural Language Semantics 2014
I. Beltagy and Raymond J. Mooney, In Proceedings of the Fourth International Workshop on Statistical Relational AI at AAAI (StarAI-2014), pp. 9--14, Quebec City, Canada, July 2014.
Inclusive yet Selective: Supervised Distributional Hypernymy Detection 2014
Stephen Roller, Katrin Erk, and Gemma Boleda, In Proceedings of the 25th International Conference on Computational Linguistics (COLING 2014), pp. 1025--1036, Dublin, Ireland, August 2014.
Integrating Language and Vision to Generate Natural Language Descriptions of Videos in the Wild 2014
Jesse Thomason, Subhashini Venugopalan, Sergio Guadarrama, Kate Saenko, and Raymond Mooney, In Proceedings of the 25th International Conference on Computational Linguistics (COLING 2014), pp. 1218--1227, Dublin, Ireland, August 2014.
Integrating Visual and Linguistic Information to Describe Properties of Objects 2014
Calvin MacKenzie, Undergraduate Honors Thesis, Computer Science Department, University of Texas at Austin.
Natural Language Semantics using Probabilistic Logic 2014
I. Beltagy, PhD proposal, Department of Computer Science, The University of Texas at Austin.
Plan Recognition Using Statistical Relational Models 2014
Sindhu Raghavan, Parag Singla, and Raymond J. Mooney, In Plan, Activity, and Intent Recognition: Theory and Practice, Sukthankar, G. and Geib, C. and Bui, H.H. and Pynadath, D. and Goldman, R.P. (Eds.), pp. 57--85, Burlington, MA 2014. Morgan Kauf...
Probabilistic Soft Logic for Semantic Textual Similarity 2014
I. Beltagy, Katrin Erk, and Raymond J. Mooney, In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (ACL-14), pp. 1210--1219, Baltimore, MD 2014.
Semantic Parsing using Distributional Semantics and Probabilistic Logic 2014
I. Beltagy, Katrin Erk, and Raymond Mooney, In Proceedings of ACL 2014 Workshop on Semantic Parsing (SP-2014), pp. 7--11, Baltimore, MD, June 2014.
Statistical Script Learning with Multi-Argument Events 2014
Karl Pichotta and Raymond J. Mooney, In Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2014), pp. 220--229, Gothenburg, Sweden, April 2014.
University of Texas at Austin KBP 2014 Slot Filling System: Bayesian Logic Programs for Textual Inference 2014
Yinon Bentor, Vidhoon Viswanathan, and Raymond Mooney , In Proceedings of the Seventh Text Analysis Conference: Knowledge Base Population (TAC 2014) 2014.
UTexas: Natural Language Semantics using Distributional Semantics and Probabilistic Logic 2014
I. Beltagy, Stephen Roller, Gemma Boleda, and Katrin Erk, and Raymond J. Mooney, In The 8th Workshop on Semantic Evaluation (SemEval-2014), pp. 796--801, Dublin, Ireland, August 2014.
Weakly-Supervised Bayesian Learning of a CCG Supertagger 2014
Dan Garrette, Chris Dyer, Jason Baldridge, and Noah A. Smith, In Proceedings of the Eighteenth Conference on Computational Natural Language Learning (CoNLL-2014), pp. 141--150, Baltimore, MD, June 2014.
A Formal Approach to Linking Logical Form and Vector-Space Lexical Semantics 2013
Dan Garrette, Katrin Erk, Raymond J. Mooney, In Computing Meaning, Harry Bunt, Johan Bos, and Stephen Pulman (Eds.), Vol. 4, pp. 27--48, Berlin 2013. Springer.
A Multimodal LDA Model Integrating Textual, Cognitive and Visual Modalities 2013
Stephen Roller and Sabine Schulte im Walde, In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing (EMNLP 2013), pp. 1146--1157, Seattle, WA, October 2013.
Adapting Discriminative Reranking to Grounded Language Learning 2013
Joohyun Kim and Raymond J. Mooney, In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (ACL-2013), pp. 218--227, Sofia, Bulgaria, August 2013.
Detecting Promotional Content in Wikipedia 2013
Shruti Bhosale, Heath Vinicombe, and Raymond J. Mooney, In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing (EMNLP 2013), pp. 1851--1857, Seattle, WA, October 2013.
Generating Natural-Language Video Descriptions Using Text-Mined Knowledge 2013
Niveda Krishnamoorthy, Girish Malkarnenkar, Raymond J. Mooney, Kate Saenko, Sergio Guadarrama, In Proceedings of the 27th AAAI Conference on Artificial Intelligence (AAAI-2013), pp. 541--547, July 2013.
Generating Natural-Language Video Descriptions Using Text-Mined Knowledge 2013
Niveda Krishnamoorthy, Girish Malkarnenkar, Raymond J. Mooney, Kate Saenko, Sergio Guadarrama, Proceedings of the NAACL HLT Workshop on Vision and Language (WVL '13) (2013), pp. 10--19.
Grounded Language Learning Models for Ambiguous Supervision 2013
Joo Hyun Kim, PhD Thesis, Department of Computer Science, University of Texas at Austin.
Identifying Phrasal Verbs Using Many Bilingual Corpora 2013
Karl Pichotta and John DeNero, In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing (EMNLP 2013), pp. 636--646, Seattle, WA, October 2013.
Learning a Part-of-Speech Tagger from Two Hours of Annotation 2013
Dan Garrette, Jason Baldridge , Proceedings of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT-13) (2013), pp. 138--147.
Montague Meets Markov: Deep Semantics with Probabilistic Logical Form 2013
I. Beltagy, Cuong Chau, Gemma Boleda, Dan Garrette, Katrin Erk, Raymond Mooney, Proceedings of the Second Joint Conference on Lexical and Computational Semantics (*Sem-2013) (2013), pp. 11--21.
Online Inference-Rule Learning from Natural-Language Extractions 2013
Sindhu Raghavan and Raymond J. Mooney, In Proceedings of the 3rd Statistical Relational AI (StaRAI-13) workshop at AAAI '13, July 2013.
Real-World Semi-Supervised Learning of POS-Taggers for Low-Resource Languages 2013
Dan Garrette, Jason Mielens, and Jason Baldridge , Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (ACL-2013) (2013), pp. 583--592.
University of Texas at Austin KBP 2013 Slot Filling System: Bayesian Logic Programs for Textual Inference 2013
Yinon Bentor, Amelia Harrison, Shruti Bhosale, and Raymond Mooney, In Proceedings of the Sixth Text Analysis Conference (TAC 2013) 2013.
Using Both Latent and Supervised Shared Topics for Multitask Learning 2013
Ayan Acharya, Aditya Rawal, Raymond J. Mooney, Eduardo R. Hruschka, In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD), pp. 369--384, Prague, Czech Republic, September 2013.
YouTube2Text: Recognizing and Describing Arbitrary Activities Using Semantic Hierarchies and Zero-shot Recognition 2013
Sergio Guadarrama, Niveda Krishnamoorthy, Girish Malkarnenkar, Subhashini Venugopalan, Raymond Mooney, Trevor Darrell, Kate Saenko, In Proceedings of the 14th International Conference on Computer Vision (ICCV-2013), pp. 2712--2719, Sydney, Australia, December 2013.
Bayesian Logic Programs for Plan Recognition and Machine Reading 2012
Sindhu Raghavan, PhD Thesis, Department of Computer Science, University of Texas at Austin. 170.
Fast Online Lexicon Learning for Grounded Language Acquisition 2012
David L. Chen, Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (ACL-2012) (2012), pp. 430--439.
Generative Models of Grounded Language Learning with Ambiguous Supervision 2012
Joohyun Kim, Technical Report, PhD proposal, Department of Computer Science, The University of Texas at Austin.
Improving Video Activity Recognition using Object Recognition and Text Mining 2012
Tanvi S. Motwani and Raymond J. Mooney, In Proceedings of the 20th European Conference on Artificial Intelligence (ECAI-2012), pp. 600--605, August 2012.
Latent Variable Models of Distributional Lexical Semantics 2012
Joseph Reisinger, PhD Thesis, Department of Computer Science, University of Texas at Austin.
Learning Language from Ambiguous Perceptual Context 2012
David L. Chen, PhD Thesis, Department of Computer Science, University of Texas at Austin. 196.
Learning to "Read Between the Lines" using Bayesian Logic Programs 2012
Sindhu Raghavan, Raymond J. Mooney, and Hyeonseo Ku, Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (ACL-2012) (2012), pp. 349--358.
Review Quality Aware Collaborative Filtering 2012
Sindhu Raghavan, Suriya Ganasekar, and Joydeep Ghosh, In Sixth ACM Conference on Recommender Systems (RecSys 2012), pp. 123--130, September 2012.
Type-Supervised Hidden Markov Models for Part-of-Speech Tagging with Incomplete Tag Dictionaries 2012
Dan Garrette and Jason Baldridge, In Proceedings of the Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL 2012), pp. 821--831, Jeju, Korea, July 2012.
Unsupervised PCFG Induction for Grounded Language Learning with Highly Ambiguous Supervision 2012
Joohyun Kim and Raymond J. Mooney, In Proceedings of the Conference on Empirical Methods in Natural Language Processing and Natural Language Learning (EMNLP-CoNLL '12), pp. 433--444, Jeju Island, Korea, July 2012.
Abductive Markov Logic for Plan Recognition 2011
Parag Singla and Raymond J. Mooney, Proceedings of the 25th AAAI Conference on Artificial Intelligence (AAAI-2011) (2011), pp. 1069-1075.
Abductive Plan Recognition by Extending Bayesian Logic Programs 2011
Sindhu Raghavan, Raymond J. Mooney, In Proceedings of the European Conference on Machine Learning/Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2011), Vol. 2, pp. 629-644, September 2011.
Building a Persistent Workforce on Mechanical Turk for Multilingual Data Collection 2011
David L. Chen and William B. Dolan, In Proceedings of The 3rd Human Computation Workshop (HCOMP 2011), August 2011.
Collecting Highly Parallel Data for Paraphrase Evaluation 2011
David L. Chen and William B. Dolan, In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, pp. 190-200, Portland, Oregon, USA, June 2011.
Constraint Propagation for Efficient Inference in Markov Logic 2011
Tivadar Papai, Parag Singla and Henry Kautz, In Proceedings of 17th International Conference on Principles and Practice of Constraint Programming (CP 2011), 6876, pp. 691-705, September 2011.
Cross-Cutting Models of Lexical Semantics 2011
Joseph Reisinger and Raymond Mooney, In Proceedings of The Conference on Empirical Methods in Natural Language Processing (EMNLP 2011), pp. 1405-1415, July 2011.
Extending Bayesian Logic Programs for Plan Recognition and Machine Reading 2011
Sindhu V. Raghavan, Technical Report, PhD proposal, Department of Computer Science, The University of Texas at Austin.
Fine-Grained Class Label Markup of Search Queries 2011
Joseph Reisinger and Marius Pasca, In Proceedings of The 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (ACL-HLT 2011), pp. 1200-1209, June 2011.
Implementing Weighted Abduction in Markov Logic 2011
James Blythe, Jerry R. Hobbs, Pedro Domingos, Rohit J. Kate, Raymond J. Mooney, In Proceedings of the International Conference on Computational Semantics, pp. 55--64, Oxford, England, January 2011.
Improving the Accuracy and Scalability of Discriminative Learning Methods for Markov Logic Networks 2011
Tuyen N. Huynh, PhD Thesis, Department of Computer Science, University of Texas at Austin.
159 pages.
Integrating Logical Representations with Probabilistic Information using Markov Logic 2011
Dan Garrette, Katrin Erk, Raymond Mooney, In Proceedings of the International Conference on Computational Semantics, pp. 105--114, Oxford, England, January 2011.
Learning to Interpret Natural Language Navigation Instructions from Observations 2011
David L. Chen and Raymond J. Mooney, Proceedings of the 25th AAAI Conference on Artificial Intelligence (AAAI-2011) (2011), pp. 859-865.
Online Max-Margin Weight Learning for Markov Logic Networks 2011
Tuyen N. Huynh and Raymond J. Mooney, In Proceedings of the Eleventh SIAM International Conference on Data Mining (SDM11), pp. 642--651, Mesa, Arizona, USA, April 2011.
Online Structure Learning for Markov Logic Networks 2011
Tuyen N. Huynh and Raymond J. Mooney, In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2011), Vol. 2, pp. 81-96, September 2011.
Panning for Gold: Finding Relevant Semantic Content for Grounded Language Learning 2011
David L. Chen and Raymond J. Mooney, In Proceedings of Symposium on Machine Learning in Speech and Language Processing (MLSLP 2011), June 2011.
A Mixture Model with Sharing for Lexical Semantics 2010
Joseph Reisinger and Raymond J. Mooney, In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP-2010), pp. 1173--1182, MIT, Massachusetts, USA, October 9--11 2010.
Authorship Attribution Using Probabilistic Context-Free Grammars 2010
Sindhu Raghavan, Adriana Kovashka and Raymond Mooney, In Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics (ACL-2010), pp. 38--42 2010.
Bayesian Abductive Logic Programs 2010
Sindhu Raghavan and Raymond Mooney, In Proceedings of the AAAI-10 Workshop on Statistical Relational AI (Star-AI 10), pp. 82--87, Atlanta, GA, July 2010.
Cross-cutting Models of Distributional Lexical Semantics 2010
Joseph S. Reisinger, unpublished. Ph.D. proposal, Department of Computer Sciences, University of Texas at Austin.
Generative Alignment and Semantic Parsing for Learning from Ambiguous Supervision 2010
Joohyun Kim and Raymond J. Mooney, In Proceedings of the 23rd International Conference on Computational Linguistics (COLING 2010), pp. 543--551, Beijing, China, August 2010.
Joint Entity and Relation Extraction using Card-Pyramid Parsing 2010
Rohit J. Kate and Raymond J. Mooney, In Proceedings of the Fourteenth Conference on Computational Natural Language Learning (CoNLL-2010), pp. 203--212, Uppsala, Sweden, July 2010.
Learning for Semantic Parsing Using Statistical Syntactic Parsing Techniques 2010
Ruifang Ge, PhD Thesis, Department of Computer Science, University of Texas at Austin. 165 pages.
Learning to Predict Readability using Diverse Linguistic Features 2010
Rohit J. Kate, Xiaoqiang Luo, Siddharth Patwardhan, Martin Franz, Radu Florian, Raymond J. Mooney, Salim Roukos and Chris Welty, In 23rd International Conference on Computational Linguistics (COLING 2010) 2010.
Multi-Prototype Vector-Space Models of Word Meaning 2010
Joseph Reisinger, Raymond J. Mooney, In Proceedings of the 11th Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-2010), pp. 109-117 2010.
Online Max-Margin Weight Learning with Markov Logic Networks 2010
Tuyen N. Huynh and Raymond J. Mooney, In Proceedings of the AAAI-10 Workshop on Statistical Relational AI (Star-AI 10), pp. 32--37, Atlanta, GA, July 2010.
Spherical Topic Models 2010
Joseph Reisinger, Austin Waters, Bryan Silverthorn, and Raymond J. Mooney, In Proceedings of the 27th International Conference on Machine Learning (ICML 2010) 2010.
Training a Multilingual Sportscaster: Using Perceptual Context to Learn Language 2010
David L. Chen, Joohyun Kim, Raymond J. Mooney, Journal of Artificial Intelligence Research, Vol. 37 (2010), pp. 397--435.
Using Closed Captions as Supervision for Video Activity Recognition 2010
Sonal Gupta, Raymond J. Mooney, In Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence (AAAI-2010), pp. 1083--1088, Atlanta, GA, July 2010.
Activity Retrieval in Closed Captioned Videos 2009
Sonal Gupta, Masters Thesis, Department of Computer Sciences, University of Texas at Austin. 64 pages.
Discriminative Learning with Markov Logic Networks 2009
Tuyen N. Huynh, unpublished. Ph.D. proposal, Department of Computer Sciences, University of Texas at Austin.
Learning a Compositional Semantic Parser using an Existing Syntactic Parser 2009
Ruifang Ge and Raymond J. Mooney, In Joint Conference of the 47th Annual Meeting of the Association for Computational Linguistics and the 4th International Joint Conference on Natural Language Processing of the Asian Federation of ...
Learning Language from Perceptual Context 2009
David L. Chen, unpublished. Ph.D. proposal, Department of Computer Sciences, University of Texas at Austin.
Learning to Disambiguate Search Queries from Short Sessions 2009
Lilyana Mihalkova and Raymond Mooney, In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), Part 2, pp. 111--127, Bled, Slovenia, September 2009.
Learning with Markov Logic Networks: Transfer Learning, Structure Learning, and an Application to Web Query Disambiguation 2009
Lilyana Mihalkova, PhD Thesis, Department of Computer Sciences, University of Texas at Austin. 176 pages.
Max-Margin Weight Learning for Markov Logic Networks 2009
Tuyen N. Huynh and Raymond J. Mooney, In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), Part 1, pp. 564--579, Bled, Slovenia, September 2009.
Max-Margin Weight Learning for Markov Logic Networks 2009
Tuyen N. Huynh and Raymond J. Mooney, In Proceedings of the International Workshop on Statistical Relational Learning (SRL-09), Leuven, Belgium, July 2009.
Probabilistic Abduction using Markov Logic Networks 2009
Rohit J. Kate and Raymond J. Mooney, In Proceedings of the IJCAI-09 Workshop on Plan, Activity, and Intent Recognition (PAIR-09), Pasadena, CA, July 2009.
Semi-supervised graph clustering: a kernel approach 2009
Brian Kulis, Sugato Basu, Inderjit Dhillon, and Raymond Mooney, Machine Learning Journal, Vol. 74, 1 (2009), pp. 1-22.
Speeding up Inference In Statistical Relational Learning by Clustering Similar Query Literals 2009
Lilyana Mihalkova and Matthew Richardson, In Proceedings of the 19th International Conference on Inductive Logic Programming (ILP-09), Leuven, Belgium, July 2009.
Spherical Topic Models 2009
Joseph Reisinger, Austin Waters, Bryan Silverthorn, and Raymond Mooney, In NIPS'09 workshop: Applications for Topic Models: Text and Beyond 2009.
Transfer Learning from Minimal Target Data by Mapping across Relational Domains 2009
Lilyana Mihalkova and Raymond Mooney, In Proceedings of the 21st International Joint Conference on Artificial Intelligence (IJCAI-09), pp. 1163--1168, Pasadena, CA, July 2009.
Using Closed Captions to Train Activity Recognizers that Improve Video Retrieval 2009
Sonal Gupta and Raymond Mooney, In Proceedings of the CVPR-09 Workshop on Visual and Contextual Learning from Annotated Images and Videos (VCL), Miami, FL, June 2009.
A Dependency-based Word Subsequence Kernel 2008
Rohit J. Kate, In Proceedings of the conference on Empirical Methods in Natural Language Processing (EMNLP-2008), pp. 400--409, Waikiki, Honolulu, Hawaii, October 2008.
Discriminative Structure and Parameter Learning for Markov Logic Networks 2008
Tuyen N. Huynh and Raymond J. Mooney, In Proceedings of the 25th International Conference on Machine Learning (ICML), Helsinki, Finland, July 2008.
Learning to Connect Language and Perception 2008
Raymond J. Mooney, In Proceedings of the 23rd AAAI Conference on Artificial Intelligence (AAAI), pp. 1598--1601, Chicago, IL, July 2008. Senior Member Paper.
Learning to Sportscast: A Test of Grounded Language Acquisition 2008
David L. Chen and Raymond J. Mooney, In Proceedings of the 25th International Conference on Machine Learning (ICML), Helsinki, Finland, July 2008.
Search Query Disambiguation from Short Sessions 2008
Lilyana Mihalkova and Raymond Mooney, In Beyond Search: Computational Intelligence for the Web Workshop at NIPS 2008.
Transfer Learning by Mapping with Minimal Target Data 2008
Lilyana Mihalkova and Raymond J. Mooney, Proceedings of the AAAI-08 Workshop on Transfer Learning For Complex Tasks (2008).
Transforming Meaning Representation Grammars to Improve Semantic Parsing 2008
Rohit J. Kate, In Proceedings of the Twelfth Conference on Computational Natural Language Learning (CoNLL-2008), pp. 33--40, Manchester, UK, August 2008.
Watch, Listen & Learn: Co-training on Captioned Images and Videos 2008
Sonal Gupta, Joohyun Kim, Kristen Grauman and Raymond Mooney, In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD), pp. 457--472, Antwerp Belgium, September 2008.
Bottom-Up Learning of Markov Logic Network Structure 2007
Lilyana Mihalkova and Raymond J. Mooney, In Proceedings of 24th International Conference on Machine Learning (ICML-2007), Corvallis, OR, June 2007.
Extracting Relations from Text: From Word Sequences to Dependency Paths 2007
Razvan C. Bunescu and Raymond J. Mooney, In Natural Language Processing and Text Mining, A. Kao and S. Poteet (Eds.), pp. 29-44, Berlin 2007. Springer Verlag.
Generation by Inverting a Semantic Parser That Uses Statistical Machine Translation 2007
Yuk Wah Wong and Raymond J. Mooney, In Proceedings of Human Language Technologies: The Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-HLT-07), pp. 172-179, Rochester, NY 2007.
Improving Learning of Markov Logic Networks using Transfer and Bottom-Up Induction 2007
Lilyana Mihalkova, Technical Report UT-AI-TR-07-341, Artificial Intelligence Lab, University of Texas at Austin.
Learning for Information Extraction: From Named Entity Recognition and Disambiguation To Relation Extraction 2007
Razvan Constantin Bunescu, PhD Thesis, Department of Computer Sciences, University of Texas at Austin. 150 pages. Also as Technical Report AI07-345, Artificial Intelligence Lab, University of Texas at Austin, August 2007.
Learning for Semantic Parsing 2007
Raymond J. Mooney, In Computational Linguistics and Intelligent Text Processing: Proceedings of the 8th International Conference (CICLing 2007), A. Gelbukh (Eds.), pp. 311--324, Mexico City, Mexico, February 2007...
Learning for Semantic Parsing and Natural Language Generation Using Statistical Machine Translation Techniques 2007
Yuk Wah Wong, PhD Thesis, Department of Computer Sciences, University of Texas at Austin. 188 pages. Also appears as Technical Report AI07-343, Artificial Intelligence Lab, University of Texas at Austin, August 200...
Learning for Semantic Parsing with Kernels under Various Forms of Supervision 2007
Rohit J. Kate, PhD Thesis, Department of Computer Sciences, University of Texas at Austin. 159 pages.
Learning Language Semantics from Ambiguous Supervision 2007
Rohit J. Kate and Raymond J. Mooney, In Proceedings of the 22nd Conference on Artificial Intelligence (AAAI-07), pp. 895-900, Vancouver, Canada, July 2007.
Learning Synchronous Grammars for Semantic Parsing with Lambda Calculus 2007
Yuk Wah Wong and Raymond J. Mooney, In Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics (ACL-2007), Prague, Czech Republic, June 2007.
Learning to Extract Relations from the Web using Minimal Supervision 2007
Razvan C. Bunescu and Raymond J. Mooney, In Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics (ACL'07), Prague, Czech Republic, June 2007.
Mapping and Revising Markov Logic Networks for Transfer Learning 2007
Lilyana Mihalkova, Tuyen N. Huynh, Raymond J. Mooney, In Proceedings of the Twenty-Second Conference on Artificial Intelligence (AAAI-07), pp. 608-614, Vancouver, BC, July 2007.
Multiple Instance Learning for Sparse Positive Bags 2007
Razvan C. Bunescu and Raymond J. Mooney, In Proceedings of the 24th Annual International Conference on Machine Learning (ICML-2007), Corvallis, OR, June 2007.
Semi-Supervised Learning for Semantic Parsing using Support Vector Machines 2007
Rohit J. Kate and Raymond J. Mooney, In Proceedings of the Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics, Short Papers (NAACL/HLT-2007), pp. 81--84, Rochester...
Statistical Relational Learning for Natural Language Information Extraction 2007
Razvan Bunescu and Raymond J. Mooney, In Introduction to Statistical Relational Learning, L. Getoor and B. Taskar (Eds.), pp. 535-552, Cambridge, MA 2007. MIT Press.
Adaptive Blocking: Learning to Scale Up Record Linkage 2006
Mikhail Bilenko, Beena Kamath, Raymond J. Mooney, In Proceedings of the Sixth IEEE International Conference on Data Mining (ICDM-06), pp. 87--96, Hong Kong, December 2006.
Discriminative Reranking for Semantic Parsing 2006
Ruifang Ge and Raymond J. Mooney, In Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics (COLING/ACL-06), Sydney, Australia, Jul...
Fast and Effective Worm Fingerprinting via Machine Learning 2006
Stewart Yang, Jianping Song, Harish Rajamani, Taewon Cho, Yin Zhang and Raymond Mooney, Technical Report AI-06-335, Artificial Intelligence Lab, The University of Texas at Austin. This is a longer version of our ICAC-2006 paper.
Fast and Effective Worm Fingerprinting via Machine Learning 2006
Stewart Yang, Jianping Song, Harish Rajamani, Taewon Cho, Yin Zhang and Raymond Mooney, In Proceedings of the 3rd IEEE International Conference on Autonomic Computing (ICAC-2006), Dublin, Ireland, June 2006. Poster Session.
Integrating Co-occurrence Statistics with Information Extraction for Robust Retrieval of Protein Interactions from Medline 2006
Razvan Bunescu, Raymond Mooney, Arun Ramani and Edward Marcotte, In Proceedings of the HLT-NAACL Workshop on Linking Natural Language Processing and Biology (BioNLP'06), pp. 49-56, New York, NY, June 2006.
Learnable Similarity Functions and Their Application to Record Linkage and Clustering 2006
Mikhail Bilenko, PhD Thesis, Department of Computer Sciences, University of Texas at Austin. 136 pages.
Learning for Semantic Parsing with Statistical Machine Translation 2006
Yuk Wah Wong and Raymond J. Mooney, In Proceedings of Human Language Technology Conference / North American Chapter of the Association for Computational Linguistics Annual Meeting (HLT-NAACL-06), pp. 439-446, New York City, NY 20...
Learning Language from Perceptual Context: A Challenge Problem for AI 2006
Raymond J. Mooney, In Proceedings of the 2006 AAAI Fellows Symposium, Boston, MA, July 2006.
Learning Semantic Parsers Using Statistical Syntactic Parsing Techniques 2006
Ruifang Ge, unpublished. Doctoral Dissertation Proposal, University of Texas at Austin" , year="2006.
Probabilistic Semi-Supervised Clustering with Constraints 2006
Sugato Basu, Mikhail Bilenko, Arindam Banerjee and Raymond J. Mooney, In Semi-Supervised Learning, O. Chapelle and B. Sch{"{o}}lkopf and A. Zien (Eds.), Cambridge, MA 2006. MIT Press.
Subsequence Kernels for Relation Extraction 2006
Razvan Bunescu and Raymond J. Mooney, In Advances in Neural Information Processing Systems, Vol. 18: Proceedings of the 2005 Conference (NIPS), Y. Weiss, B. Schoelkopf, J. Platt (Eds.) 2006.
Transfer Learning with Markov Logic Networks 2006
Lilyana Mihalkova and Raymond Mooney, In Proceedings of the ICML-06 Workshop on Structural Knowledge Transfer for Machine Learning, Pittsburgh, PA, June 2006.
Using Active Relocation to Aid Reinforcement Learning 2006
Lilyana Mihalkova and Raymond Mooney, In Prodeedings of the 19th International FLAIRS Conference (FLAIRS-2006), pp. 580-585, Melbourne Beach, FL, May 2006.
Using Encyclopedic Knowledge for Named Entity Disambiguation 2006
Razvan Bunescu and Marius Pasca, In Proceesings of the 11th Conference of the European Chapter of the Association for Computational Linguistics (EACL-06), pp. 9-16, Trento, Italy 2006.
Using String-Kernels for Learning Semantic Parsers 2006
Rohit J. Kate and Raymond J. Mooney, In ACL 2006: Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the ACL, pp. 913-920, Morristown, NJ, USA 2006. Association for Computa...
A Kernel-based Approach to Learning Semantic Parsers 2005
Rohit J. Kate, unpublished. Doctoral Dissertation Proposal, University of Texas at Austin.
A Shortest Path Dependency Kernel for Relation Extraction 2005
R. C. Bunescu, and Raymond J. Mooney, In Proceedings of the Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP-05), pp. 724-731, Vancouver, BC, October 2005.
A Statistical Semantic Parser that Integrates Syntax and Semantics 2005
Ruifang Ge and Raymond J. Mooney, In Proceedings of CoNLL-2005, Ann Arbor, Michigan, June 2005.
Active Learning for Probability Estimation using Jensen-Shannon Divergence 2005
P. Melville, S. M. Yang, M. Saar-Tsechansky and Raymond J. Mooney, In Proceedings of the 16th European Conference on Machine Learning, pp. 268--279, Porto, Portugal, October 2005.
Adaptive Product Normalization: Using Online Learning for Record Linkage in Comparison Shopping 2005
Mikhail Bilenko, Sugato Basu, and Mehran Sahami, In Proceedings of the 5th International Conference on Data Mining (ICDM-2005), pp. 58--65, Houston, TX, November 2005.
Alignments and String Similarity in Information Integration: A Random Field Approach 2005
Mikhail Bilenko and Raymond J. Mooney, In Proceedings of the 2005 Dagstuhl Seminar on Machine Learning for the Semantic Web, Dagstuhl, Germany, February 2005.
An Expected Utility Approach to Active Feature-value Acquisition 2005
P. Melville, M. Saar-Tsechansky, F. Provost and Raymond J. Mooney, In Proceedings of the International Conference on Data Mining, pp. 745-748, Houston, TX, November 2005.
Combining Bias and Variance Reduction Techniques for Regression 2005
Y. L. Suen, P. Melville and Raymond J. Mooney, In Proceedings of the 16th European Conference on Machine Learning, pp. 741-749, Porto, Portugal, October 2005.
Combining Bias and Variance Reduction Techniques for Regression 2005
Yuk Lai Suen, Prem Melville and Raymond J. Mooney, Technical Report UT-AI-TR-05-321, University of Texas at Austin. www.cs.utexas.edu/~ml/publication.
Comparative Experiments on Learning Information Extractors for Proteins and their Interactions 2005
Razvan Bunescu, Ruifang Ge, Rohit J. Kate, Edward M. Marcotte, Raymond J. Mooney, Arun Kumar Ramani, and Yuk Wah Wong, Artificial Intelligence in Medicine (special issue on Summarization and Information Extraction from Medical Documents), 2 (2005), pp. 139-155.
Consolidating the Set of Known Human Protein-Protein Interactions in Preparation for Large-Scale Mapping of the Human Interactome 2005
A.K. Ramani, R.C. Bunescu, Raymond J. Mooney and E.M. Marcotte, Genome Biology, Vol. 6, 5 (2005), pp. r40.
Creating Diverse Ensemble Classifiers to Reduce Supervision 2005
Prem Melville, PhD Thesis, Department of Computer Sciences, University of Texas at Austin. 141 pages. Technical Report TR-05-49.
Economical Active Feature-value Acquisition through Expected Utility Estimation 2005
P. Melville, M. Saar-Tsechansky, F. Provost and Raymond J. Mooney, In Proceedings of the KDD-05 Workshop on Utility-Based Data Mining, pp. 10-16, Chicago, IL, August 2005.
Explaining Recommendations: Satisfaction vs. Promotion 2005
Mustafa Bilgic and Raymond J. Mooney, In Proceedings of Beyond Personalization 2005: A Workshop on the Next Stage of Recommender Systems Research at the 2005 International Conference on Intelligent User Interfaces, San Diego, CA, J...
Learning for Collective Information Extraction 2005
Razvan C. Bunescu, Technical Report TR-05-02, Department of Computer Sciences, University of Texas at Austin. Ph.D. proposal.
Learning for Semantic Parsing Using Statistical Machine Translation Techniques 2005
Yuk Wah Wong, unpublished. Doctoral Dissertation Proposal, University of Texas at Austin.
Learning to Transform Natural to Formal Languages 2005
Rohit J. Kate, Yuk Wah Wong and Raymond J. Mooney, In Proceedings of the Twentieth National Conference on Artificial Intelligence (AAAI-05), pp. 1062-1068, Pittsburgh, PA, July 2005.
Mining Knowledge from Text Using Information Extraction 2005
Raymond J. Mooney and R. Bunescu, SIGKDD Explorations (special issue on Text Mining and Natural Language Processing), Vol. 7, 1 (2005), pp. 3-10.
Model-based Overlapping Clustering 2005
A. Banerjee, C. Krumpelman, S. Basu, Raymond J. Mooney and Joydeep Ghosh, In Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-05) 2005.
Semi-supervised Clustering: Probabilistic Models, Algorithms and Experiments 2005
Sugato Basu, PhD Thesis, University of Texas at Austin.
Semi-supervised Graph Clustering: A Kernel Approach 2005
B. Kulis, S. Basu, I. Dhillon and Raymond J. Mooney, In Proceedings of the 22nd International Conference on Machine Learning, pp. 457--464, Bonn, Germany, August 2005. (Distinguished Student Paper Award).
Towards Self-Configuring Hardware for Distributed Computer Systems 2005
Jonathan Wildstrom, Peter Stone, E. Witchel, Raymond Mooney and M. Dahlin, In The Second International Conference on Autonomic Computing, pp. 241-249, June 2005.
Using Biomedical Literature Mining to Consolidate the Set of Known Human Protein-Protein Interactions 2005
A. Ramani, E. Marcotte, R. Bunescu and Raymond J. Mooney, In Proceedings of the ISMB/ACL-05 Workshop of the BioLINK SIG: Linking Literature, Information and Knowledge for Biology, Detroit, MI, June 2005.
A Comparison of Inference Techniques for Semi-supervised Clustering with Hidden Markov Random Fields 2004
Mikhail Bilenko and Sugato Basu, In Proceedings of the ICML-2004 Workshop on Statistical Relational Learning and its Connections to Other Fields (SRL-2004), Banff, Canada, July 2004.
A Probabilistic Framework for Semi-Supervised Clustering 2004
Sugato Basu, Mikhail Bilenko, and Raymond J. Mooney, In Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2004), pp. 59-68, Seattle, WA, August 2004.
Active Feature-Value Acquisition for Classifier Induction 2004
Prem Melville, Maytal Saar-Tsechansky, Foster Provost, and Raymond J. Mooney, Technical Report UT-AI-TR-04-311, Artificial Intelligence Lab, University of Texas at Austin.
Active Feature-Value Acquisition for Classifier Induction 2004
Prem Melville, Maytal Saar-Tsechansky, Foster Provost, and Raymond J. Mooney, In Proceedings of the Fourth IEEE International Conference on Data Mining (ICDM-2004), pp. 483-486, Brighton, UK, November 2004.
Active Semi-Supervision for Pairwise Constrained Clustering 2004
Sugato Basu, Arindam Banerjee, and Raymond J. Mooney, In Proceedings of the 2004 SIAM International Conference on Data Mining (SDM-04), April 2004.
Collective Information Extraction with Relational Markov Networks 2004
Razvan Bunescu and Raymond J. Mooney, In Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL-04), pp. 439-446, Barcelona, Spain, July 2004.
Creating Diversity in Ensembles Using Artificial Data 2004
Prem Melville and Raymond J. Mooney, Journal of Information Fusion: Special Issue on Diversity in Multi Classifier Systems, Vol. 6, 1 (2004), pp. 99-111.
Diverse Ensembles for Active Learning 2004
Prem Melville and Raymond J. Mooney, In Proceedings of 21st International Conference on Machine Learning (ICML-2004), pp. 584-591, Banff, Canada, July 2004.
Experiments on Ensembles with Missing and Noisy Data 2004
Prem Melville, Nishit Shah, Lilyana Mihalkova, and Raymond J. Mooney, In {Lecture Notes in Computer Science:} Proceedings of the Fifth International Workshop on Multi Classifier Systems (MCS-2004), F. Roli, J. Kittler, and T. Windeatt (Eds.), Vol. 3077, pp. 293-3...
Explanation for Recommender Systems: Satisfaction vs. Promotion 2004
Mustafa Bilgic, unpublished. Undergraduate Honor Thesis, Department of Computer Sciences, University of Texas at Austin.
Guiding a Reinforcement Learner with Natural Language Advice: Initial Results in RoboCup Soccer 2004
Gregory Kuhlmann, Peter Stone, Raymond J. Mooney, and Jude W. Shavlik, In The AAAI-2004 Workshop on Supervisory Control of Learning and Adaptive Systems, July 2004.
Integrating Constraints and Metric Learning in Semi-Supervised Clustering 2004
Mikhail Bilenko, Sugato Basu, and Raymond J. Mooney, In Proceedings of 21st International Conference on Machine Learning (ICML-2004), pp. 81-88, Banff, Canada, July 2004.
Learnable Similarity Functions and Their Applications to Clustering and Record Linkage 2004
Mikhail Bilenko, In Proceedings of the Ninth AAAI/SIGART Doctoral Consortium, pp. 981--982, San Jose, CA, July 2004.
Learning Semantic Parsers: An Important But Under-Studied Problem 2004
Raymond J. Mooney, In Papers from the AAAI 2004 Spring Symposium on Language Learning: An Interdisciplinary Perspective, pp. 39--44, Stanford, CA, March 2004.
Learning Transformation Rules for Semantic Parsing 2004
Rohit J. Kate, Yuk Wah Wong, Ruifang Ge, and Raymond J. Mooney, unpublished. Unpublished Technical Report.
Relational Data Mining with Inductive Logic Programming for Link Discovery 2004
Raymond J. Mooney, P. Melville, L. R. Tang, J. Shavlik, I. Dutra and D. Page, Data Mining: Next Generation Challenges and Future DirectionsKargupta, H., Joshi, A., Sivakumar K., and Yesha, Y. (Eds.) (2004), pp. 239--254. AAAI Press.
Relational Markov Networks for Collective Information Extraction 2004
Razvan Bunescu and Raymond J. Mooney, In Proceedings of the ICML-04 Workshop on Statistical Relational Learning and its Connections to Other Fields, Banff, Alberta, July 2004.
Semi-supervised Clustering with Limited Background Knowledge 2004
Sugato Basu, In Proceedings of the Ninth AAAI/SIGART Doctoral Consortium, pp. 979--980, San Jose, CA, July 2004.
Semi-supervised Clustering: Learning with Limited User Feedback 2004
Sugato Basu, Technical Report, Cornell University.
Semisupervised Clustering for Intelligent User Management 2004
Sugato Basu, Mikhail Bilenko, and Raymond J. Mooney, In Proceedings of the IBM Austin Center for Advanced Studies 5th Annual Austin CAS Conference, Austin, TX, February 2004.
Text Mining with Information Extraction 2004
Un Yong Nahm, PhD Thesis, Department of Computer Sciences, University of Texas at Austin. 217 pages. Also appears as Technical Report UT-AI-TR-04-311.
Using Soft-Matching Mined Rules to Improve Information Extraction 2004
Un Yong Nahm and Raymond J. Mooney, In Proceedings of the AAAI-2004 Workshop on Adaptive Text Extraction and Mining (ATEM-2004), pp. 27-32, San Jose, CA, July 2004.
Acquiring Word-Meaning Mappings for Natural Language Interfaces 2003
Cynthia A. Thompson and Raymond J. Mooney, Journal of Artificial Intelligence Research, Vol. 18 (2003), pp. 1-44.
Adaptive Duplicate Detection Using Learnable String Similarity Measures 2003
Mikhail Bilenko and Raymond J. Mooney, In Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2003), pp. 39-48, Washington, DC, August 2003.
Adaptive Name-Matching in Information Integration 2003
Mikhail Bilenko, William W. Cohen, Stephen Fienberg, Raymond J. Mooney, and Pradeep Ravikumar, IEEE Intelligent Systems, Vol. 18, 5 (2003), pp. 16-23.
Associative Anaphora Resolution: A Web-Based Approach 2003
Razvan Bunescu, In Proceedings of the EACL-2003 Workshop on the Computational Treatment of Anaphora, pp. 47-52, Budapest, Hungary 2003.
Bottom-Up Relational Learning of Pattern Matching Rules for Information Extraction 2003
Mary Elaine Califf and Raymond J. Mooney, Journal of Machine Learning Research (2003), pp. 177-210.
Comparing and Unifying Search-Based and Similarity-Based Approaches to Semi-Supervised Clustering 2003
Sugato Basu, Mikhail Bilenko, and Raymond J. Mooney, In Proceedings of the ICML-2003 Workshop on the Continuum from Labeled to Unlabeled Data in Machine Learning and Data Mining, pp. 42-49, Washington, DC 2003.
Constructing Diverse Classifier Ensembles Using Artificial Training Examples 2003
Prem Melville and Raymond J. Mooney, In Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence (IJCAI-2003), pp. 505-510, Acapulco, Mexico, August 2003.
Creating Diverse Ensemble Classifiers 2003
Prem Melville, Technical Report UT-AI-TR-03-306, Department of Computer Sciences, University of Texas at Austin. Ph.D. proposal.
Employing Trainable String Similarity Metrics for Information Integration 2003
Mikhail Bilenko and Raymond J. Mooney, In Proceedings of the IJCAI-03 Workshop on Information Integration on the Web, pp. 67-72, Acapulco, Mexico, August 2003.
Integrating Top-down and Bottom-up Approaches in Inductive Logic Programming: Applications in Natural Language Processing and Relational Data Mining 2003
Lappoon R. Tang, PhD Thesis, Department of Computer Sciences, University of Texas.
Learnable Similarity Functions and Their Applications to Record Linkage and Clustering 2003
Mikhail Bilenko, unpublished. Doctoral Dissertation Proposal, University of Texas at Austin.
Learning to Extract Proteins and their Interactions from Medline Abstracts 2003
Razvan Bunescu, Ruifang Ge, Rohit J. Kate, Raymond J. Mooney, Yuk Wah Wong, Edward M. Marcotte, and Arun Kumar Ramani, In Proceedings of the ICML-03 Workshop on Machine Learning in Bioinformatics, pp. 46-53, Washington, DC, August 2003.
Machine Learning 2003
Raymond J. Mooney, , McGraw-Hill, New York, NY 2003. McGraw-Hill.
On Evaluation and Training-Set Construction for Duplicate Detection 2003
Mikhail Bilenko and Raymond J. Mooney, In Proceedings of the KDD-03 Workshop on Data Cleaning, Record Linkage, and Object Consolidation, pp. 7-12, Washington, DC, August 2003.
Scaling Up ILP to Large Examples: Results on Link Discovery for Counter-Terrorism 2003
Lappoon R. Tang, Raymond J. Mooney, and Prem Melville, In Proceedings of the KDD-2003 Workshop on Multi-Relational Data Mining (MRDM-2003), pp. 107--121, Washington DC, August 2003.
Text Mining with Information Extraction 2003
Raymond J. Mooney and Un Yong Nahm, In Multilingualism and Electronic Language Management: Proceedings of the 4th International MIDP Colloquium, W. Daelemans and T. du Plessis and C. Snyman and L. Teck (Eds.), pp. 141-160, Bloemf...
Content-Boosted Collaborative Filtering for Improved Recommendations 2002
Prem Melville, Raymond J. Mooney, and Ramadass Nagarajan, In Proceedings of the Eighteenth National Conference on Artificial Intelligence (AAAI-02), pp. 187-192, Edmonton, Alberta 2002.
Extracting Gene and Protein Names from Biomedical Abstracts 2002
Razvan Bunescu, Ruifang Ge, Raymond J. Mooney, Edward Marcotte, and Arun Kumar Ramani, unpublished. Unpublished Technical Note.
Learning to Combine Trained Distance Metrics for Duplicate Detection in Databases 2002
Mikhail Bilenko and Raymond J. Mooney, Technical Report AI 02-296, Artificial Intelligence Laboratory, University of Texas at Austin.
Mining Soft-Matching Association Rules 2002
Un Yong Nahm and Raymond J. Mooney, In Proceedings of the Eleventh International Conference on Information and Knowledge Management (CIKM-2002), pp. 681-683, McLean, VA, November 2002.
Property-Based Feature Engineering and Selection 2002
Noppadon Kamolvilassatian, Masters Thesis, Department of Computer Sciences, University of Texas at Austin. 85 pages.
Relational Data Mining with Inductive Logic Programming for Link Discovery 2002
Raymond J. Mooney, Prem Melville, Lappoon R. Tang, Jude Shavlik, Inês de Castro Dutra, David Page, and Vítor Santos Costa, In Proceedings of the National Science Foundation Workshop on Next Generation Data Mining, Baltimore, MD, November 2002.
Semi-supervised Clustering by Seeding 2002
Sugato Basu, Arindam Banerjee, and Raymond J. Mooney, In Proceedings of 19th International Conference on Machine Learning (ICML-2002), pp. 19-26 2002.
Text Mining with Information Extraction 2002
Un Yong Nahm and Raymond J. Mooney, In Proceedings of the AAAI 2002 Spring Symposium on Mining Answers from Texts and Knowledge Bases, pp. 60-67, Stanford, CA, March 2002.
Two Approaches to Handling Noisy Variation in Text Mining 2002
Un Yong Nahm, Mikhail Bilenko, and Raymond J. Mooney, In Papers from the Nineteenth International Conference on Machine Learning (ICML-2002) Workshop on Text Learning, pp. 18-27, Sydney, Australia, July 2002.
Content-Boosted Collaborative Filtering 2001
Prem Melville, Raymond J. Mooney, and Ramadass Nagarajan, In Proceedings of the SIGIR-2001 Workshop on Recommender Systems, New Orleans, LA, September 2001.
ELIXIR: A Library for Writing Wrappers in Java 2001
Edward Wild, Undergraduate Honor Thesis, Department of Computer Sciences, University of Texas at Austin.
Evaluating the Novelty of Text-Mined Rules using Lexical Knowledge 2001
Sugato Basu, Raymond J. Mooney, Krupakar V. Pasupuleti, and Joydeep Ghosh, In Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2001), pp. 233-239, San Francisco, CA 2001.
Mining Soft-Matching Rules from Textual Data 2001
Un Yong Nahm and Raymond J. Mooney, In Proceedings of the 18th International Joint Conference on Artificial Intelligence 2001.
Text Mining with Information Extraction 2001
Un Yong Nahm, unpublished. Ph.D. proposal, Department of Computer Sciences, University of Texas at Austin.
Using Lexical Knowlege to Evaluate the Novelty of Rules Mined from Text 2001
Sugato Basu, Raymond J. Mooney, Krupakar V. Pasupuleti, and Joydeep Ghosh, In Proceedings of NAACL 2001 Workshop on WordNet and Other Lexical Resources: Applications, Extensions and Customizations, pp. 144--149, Pittsburg, PA, June 2001.
Using Multiple Clause Constructors in Inductive Logic Programming for Semantic Parsing 2001
Lappoon R. Tang and Raymond J. Mooney, In Proceedings of the 12th European Conference on Machine Learning, pp. 466-477, Freiburg, Germany 2001.
A Mutually Beneficial Integration of Data Mining and Information Extraction 2000
Un Yong Nahm and Raymond J. Mooney, In Proceedings of the Seventeenth National Conference on Artificial Intelligence (AAAI-00), pp. 627-632, Austin, TX, July 2000.
Automated Construction of Database Interfaces: Integrating Statistical and Relational Learning for Semantic Parsing 2000
Lappoon R. Tang and Raymond J. Mooney, In Proceedings of the Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora(EMNLP/VLC-2000), pp. 133-141, Hong Kong, October 2000.
Content-Based Book Recommending Using Learning for Text Categorization 2000
Raymond J. Mooney and Loriene Roy, In Proceedings of the Fifth ACM Conference on Digital Libraries, pp. 195-204, San Antonio, TX, June 2000.
Integrating Abduction and Induction in Machine Learning 2000
Raymond J. Mooney, In Abduction and Induction, P. A. Flach and A. C. Kakas (Eds.), pp. 181-191 2000. Kluwer Academic Publishers.
Integrating Statistical and Relational Learning for Semantic Parsing: Applications to Learning Natural Language Interfaces for Databases 2000
Lappoon R. Tang, unpublished. Ph.D. proposal, Department of Computer Sciences, University of Texas at Austin.
Learning for Semantic Interpretation: Scaling Up Without Dumbing Down 2000
Raymond J. Mooney, In Workshop Notes for the Workshop on Learning Language in Logic, pp. 7-15, Bled, Slovenia 2000.
Using Information Extraction to Aid the Discovery of Prediction Rules from Text 2000
Un Yong Nahm and Raymond J. Mooney, In Proceedings of the Sixth International Conference on Knowledge Discovery and Data Mining (KDD-2000) Workshop on Text Mining, pp. 51--58, Boston, MA, August 2000.
Active Learning for Natural Language Parsing and Information Extraction 1999
Cynthia A. Thompson, Mary Elaine Califf and Raymond J. Mooney, In Proceedings of the Sixteenth International Conference on Machine Learning (ICML-99), pp. 406-414, Bled, Slovenia, June 1999.
Automatic Construction of Semantic Lexicons for Learning Natural Language Interfaces 1999
Cynthia A. Thompson and Raymond J. Mooney, In Proceedings of the Sixteenth National Conference on Artificial Intelligence (AAAI-99), pp. 487-493, Orlando, FL, July 1999.
Content-Based Book Recommending Using Learning for Text Categorization 1999
Raymond J. Mooney and Loriene Roy, In Proceedings of the SIGIR-99 Workshop on Recommender Systems: Algorithms and Evaluation, Berkeley, CA, August 1999.
Relational Learning of Pattern-Match Rules for Information Extraction 1999
Mary Elaine Califf and Raymond J. Mooney, In Proceedings of the Sixteenth National Conference on Artificial Intelligence (AAAI-99), pp. 328-334, Orlando, FL, July 1999.
Using HTML Structure and Linked Pages to Improve Learning for Text Categorization 1999
Michael B. Cline, Technical Report AI 98-270, Department of Computer Sciences, University of Texas at Austin. Undergraduate Honors Thesis.
Advantages of Decision Lists and Implicit Negatives in Inductive Logic Programming 1998
Mary Elaine Califf and Raymond J. Mooney, New Generation Computing, Vol. 16, 3 (1998), pp. 263-281.
An Experimental Comparison of Genetic Programming and Inductive Logic Programming on Learning Recursive List Functions 1998
Lappoon R. Tang, Mary Elaine Califf, and Raymond J. Mooney, Technical Report AI 98-271, Artificial Intelligence Lab, University of Texas at Austin.
Book Recommending Using Text Categorization with Extracted Information 1998
Raymond J. Mooney, Paul N. Bennett, and Loriene Roy, In Proceedings of the Fifteenth National Conference on Artificial Intelligence (AAAI-98)"-REC-WKSHP98, year="1998, pp. 70-74, Madison, WI 1998.
Relational Learning of Pattern-Match Rules for Information Extraction 1998
Mary Elaine Califf and Raymond J. Mooney, In Proceedings of AAAI Spring Symposium on Applying Machine Learning to Discourse Processing, pp. 6-11, Standford, CA, March 1998.
Relational Learning Techniques for Natural Language Information Extraction 1998
Mary Elaine Califf, PhD Thesis, Department of Computer Sciences, University of Texas. 142 pages. Also appears as Artificial Intelligence Laboratory Technical Report AI 98-276.
Semantic Lexicon Acquisition for Learning Natural Language Interfaces 1998
Cynthia Ann Thompson, PhD Thesis, Department of Computer Sciences, University of Texas at Austin. 101 pages. Also appears as Technical Report AI 99-278, Artificial Intelligence Lab, University of Texas at Austin.
Semantic Lexicon Acquisition for Learning Natural Language Interfaces 1998
Cynthia A. Thompson and Raymond J. Mooney, In Proceedings of the Sixth Workshop on Very Large Corpora, Montreal, Quebec, Canada, August 1998. Also available as TR AI 98-273, Artificial Intelligence Lab, University of Texas at Austin, M...
Text Categorization Through Probabilistic Learning: Applications to Recommender Systems 1998
Paul N. Bennett, unpublished. Honors thesis, Department of Computer Sciences, The University of Texas at Austin.
Theory Refinement for Bayesian Networks with Hidden Variables 1998
Sowmya Ramachandran and Raymond J. Mooney, In Proceedings of the Fifteenth International Conference on Machine Learning (ICML-98), pp. 454--462, Madison, WI, July 1998.
Theory Refinement of Bayesian Networks with Hidden Variables 1998
Sowmya Ramachandran and Raymond J. Mooney, PhD Thesis, Department of Computer Sciences, University of Texas at Austin. 139 pages. Also appears as Technical Report AI 98-265, Artificial Intelligence Lab, University of Texas at Austin.
Using Multi-Strategy Learning to Improve Planning Efficiency and Quality 1998
Tara A. Estlin, PhD Thesis, Department of Computer Sciences, The University of Texas at Austin.
An Inductive Logic Programming Method for Corpus-based Parser Construction 1997
John M. Zelle and Raymond J. Mooney, unpublished. Unpublished Technical Note.
Applying ILP-based Techniques to Natural Language Information Extraction: An Experiment in Relational Learning 1997
Mary Elaine Califf and Raymond J. Mooney, In Workshop Notes of the IJCAI-97 Workshop on Frontiers of Inductive Logic Programming, pp. 7--11, Nagoya, Japan, August 1997.
Integrating Abduction and Induction in Machine Learning 1997
Raymond J. Mooney, In Working Notes of the IJCAI-97 Workshop on Abduction and Induction in AI, pp. 37--42, Nagoya, Japan, August 1997.
Learning Parse and Translation Decisions From Examples With Rich Context 1997
Ulf Hermjakob, PhD Thesis, Department of Computer Sciences, The University of Texas at Austin. 175 pages. Technical Report UT-AI97-261.
Learning Parse and Translation Decisions From Examples With Rich Context 1997
Ulf Hermjakob and Raymond J. Mooney, In Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics (ACL'97/EACL'97), pp. 482-489, July 1997.
Learning to Improve both Efficiency and Quality of Planning 1997
Tara A. Estlin and Raymond J. Mooney, In Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence (IJCAI-97), pp. 1227-1232, Nagoya, Japan 1997.
Learning to Parse Natural Language Database Queries into Logical Form 1997
Cynthia A. Thompson, Raymond J. Mooney, and Lappoon R. Tang, In Proceedings of the ML-97 Workshop on Automata Induction, Grammatical Inference, and Language Acquisition, Nashville, TN, July 1997.
Parameter Revision Techniques for Bayesian Networks with Hidden Variables: An Experimental Comparison 1997
Sowmya Ramachandran and Raymond J. Mooney, unpublished. Unpublished Technical Note.
Relational Learning of Pattern-Match Rules for Information Extraction 1997
Mary Elaine Califf and Raymond J. Mooney, In Proceedings of the ACL Workshop on Natural Language Learning, pp. 9-15, Madrid, Spain, July 1997.
Relational Learning Techniques for Natural Language Information Extraction 1997
Mary Elaine Califf, unpublished. Ph.D. proposal, Department of Computer Sciences, University of Texas at Austin.
Semantic Lexicon Acquisition for Learning Parsers 1997
Cynthia A. Thompson and Raymond J. Mooney, unpublished. Submitted for review.
A Novel Application of Theory Refinement to Student Modeling 1996
Paul Baffes and Raymond J. Mooney, In Proceedings of the Thirteenth National Conference on Artificial Intelligence (AAAI-96), pp. 403-408, Portland, OR, August 1996.
Advantages of Decision Lists and Implicit Negative in Inductive Logic Programming 1996
Mary Elaine Califf and Raymond J. Mooney, Technical Report, Artificial Intelligence Lab, University of Texas at Austin.
Combining Symbolic and Connectionist Learning Methods to Refine Certainty-Factor Rule-Bases 1996
J. Jeffrey Mahoney, PhD Thesis, Department of Computer Sciences, University of Texas at Austin. 113 pages.
Comparative Experiments on Disambiguating Word Senses: An Illustration of the Role of Bias in Machine Learning 1996
Raymond J. Mooney, In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP-96), pp. 82-91, Philadelphia, PA 1996.
Comparative Results on Using Inductive Logic Programming for Corpus-based Parser Construction 1996
John M. Zelle and Raymond J. Mooney, In Connectionist, Statistical, and Symbolic Approaches to Learning for Natural Language Processing, Stefan Wermter and Ellen Riloff and Gabriela Scheler (Eds.), pp. 355-369, Berlin 1996. Spri...
Corpus-Based Lexical Acquisition For Semantic Parsing 1996
Cynthia Thompson, unpublished. Ph.D. proposal.
Hybrid Learning of Search Control for Partial-Order Planning 1996
Tara A. Estlin and Raymond J. Mooney, In New Directions in AI Planning, Malik Ghallab and Alfredo Milani (Eds.), pp. 129-140, Amsterdam 1996. IOS Press.
Inductive Logic Programming for Natural Language Processing 1996
Raymond J. Mooney, In Inductive Logic Programming: Selected papers from the 6th International Workshop, Stephen Muggleton (Eds.), pp. 3-22, Berlin 1996. Springer Verlag.
Integrating EBL and ILP to Acquire Control Rules for Planning 1996
Tara A. Estlin and Raymond J. Mooney, Proceedings of the Third International Workshop on Multi-Strategy Learning (MSL-96) (1996), pp. 271--279.
Integrating Explanation-Based and Inductive Learning Techniques to Acquire Search-Control for Planning 1996
Tara A. Estlin, Technical Report AI96-250, Department of Computer Sciences, University of Texas.
Learning the Past Tense of English Verbs Using Inductive Logic Programming 1996
Raymond J. Mooney and Mary Elaine Califf, In Connectionist, Statistical, and Symbolic Approaches to Learning for Natural Language Processing, {S. Wermter, E. Riloff} and G. Scheler (Eds.), pp. 370-384, Berlin 1996. Springer.
Learning to Parse Database Queries using Inductive Logic Programming 1996
John M. Zelle and Raymond J. Mooney, In AAAI/IAAI, pp. 1050-1055, Portland, OR, August 1996. AAAI Press/MIT Press.
Lexical Acquisition: A Novel Machine Learning Problem 1996
Cynthia A. Thompson and Raymond J. Mooney, Technical Report, Artificial Intelligence Lab, University of Texas at Austin.
Multi-Strategy Learning of Search Control for Partial-Order Planning 1996
Tara A. Estlin and Raymond J. Mooney, In Proceedings of the Thirteenth National Conference on Artificial Intelligence (AAAI-96), pp. 843-848, Portland, OR, August 1996.
Qualitative Multiple-Fault Diagnosis of Continuous Dynamic Systems Using Behavioral Modes 1996
Siddarth Subramanian and Raymond J. Mooney, In Proceedings of the Thirteenth National Conference on Artificial Intelligence (AAAI-96), pp. 965-970, Portland, OR, August 1996.
Refinement-Based Student Modeling and Automated Bug Library Construction 1996
Paul Baffes and Raymond Mooney, Journal of Artificial Intelligence in Education, Vol. 7, 1 (1996), pp. 75-116.
Revising Bayesian Network Parameters Using Backpropagation 1996
Sowmya Ramachandran and Raymond J. Mooney, In Proceedings of the International Conference on Neural Networks (ICNN-96), Special Session on Knowledge-Based Artificial Neural Networks, pp. 82--87, Washington DC, June 1996.
A Comparison of Two Methods Employing Inductive Logic Programming for Corpus-based Parser Constuction 1995
John M. Zelle and Raymond J. Mooney, In Working Notes of the IJCAI-95 Workshop on New Approaches to Learning for Natural Language Processing, pp. 79--86, Montreal, Quebec, Canada, August 1995.
A Preliminary PAC Analysis of Theory Revision 1995
Raymond J. Mooney, In Computational Learning Theory and Natural Learning Systems, Vol. 3, T. Petsche and S. Hanson and Jude W. Shavlik (Eds.), pp. 43-53, Cambridge, MA 1995. MIT Press.
Acquisition of a Lexicon from Semantic Representations of Sentences 1995
Cynthia A. Thompson, In Proceedings of the 33rd Annual Meeting of the Association for Computational Linguistics (ACL-95), pp. 335-337, Cambridge, MA 1995.
Automated Refinement of First-Order Horn-Clause Domain Theories 1995
Bradley L. Richards and Raymond J. Mooney, Machine Learning, Vol. 19, 2 (1995), pp. 95-131.
Encouraging Experimental Results on Learning CNF 1995
Raymond J. Mooney, Machine Learning, Vol. 19, 1 (1995), pp. 79-92.
Inducing Logic Programs without Explicit Negative Examples 1995
John M. Zelle, Cynthia A. Thompson, Mary Elaine Califf, and Raymond J. Mooney, In Proceedings of the Fifth International Workshop on Inductive Logic Programming (ILP-95), pp. 403-416, Leuven, Belgium 1995.
Induction of First-Order Decision Lists: Results on Learning the Past Tense of English Verbs 1995
Raymond J. Mooney and Mary Elaine Califf, Journal of Artificial Intelligence Research, Vol. 3 (1995), pp. 1-24.
Multiple-Fault Diagnosis Using General Qualitative Models with Fault Modes 1995
Siddarth Subramanian and Raymond J. Mooney, In Working Notes of the IJCAI-95 Workshop on Engneering Problems for Qualitative Reasoning, Monreal, Quebec, August 1995.
Qualitative Multiple-Fault Diagnosis of Continuous Dynamic Systems Using Behavioral Modes 1995
Siddarth Subramanian, PhD Thesis, Department of Computer Sciences, University of Texas at Austin. 128 pages. Also appears as Technical Report AI 95-239.
Refinement of Bayesian Networks by Combining Connectionist and Symbolic Techniques 1995
Sowmya Ramachandran, Unpublished Ph.D. Thesis Proposal.
Using Inductive Logic Programming to Automate the Construction of Natural Language Parsers 1995
John M. Zelle, PhD Thesis, Department of Computer Sciences, The University of Texas at Austin.
A Multistrategy Approach to Theory Refinement 1994
Raymond J. Mooney and Dirk Ourston, In Machine Learning: A Multistrategy Approach, Vol. IV, Ryszard S. Michalski and G. Teccuci (Eds.), pp. 141-164, San Mateo, CA 1994. Morgan Kaufmann.
Automatic Student Modeling and Bug Library Construction using Theory Refinement 1994
Paul T. Baffes, PhD Thesis, Department of Computer Sciences, The University of Texas at Austin.
Combining Top-Down And Bottom-Up Techniques In Inductive Logic Programming 1994
John M. Zelle, Raymond J. Mooney, and Joshua B. Konvisser, In Proceedings of the Eleventh International Workshop on Machine Learning (ML-94), pp. 343--351, Rutgers, NJ, July 1994.
Comparing Methods For Refining Certainty Factor Rule-Bases 1994
J. Jeffrey Mahoney and Raymond J. Mooney, In Proceedings of the Eleventh International Workshop on Machine Learning (ML-94), pp. 173--180, Rutgers, NJ, July 1994.
Inducing Deterministic Prolog Parsers From Treebanks: A Machine Learning Approach 1994
John M. Zelle and Raymond J. Mooney, Proceedings of the Twelfth National Conference on Artificial Intelligence (AAAI-94) (1994), pp. 748--753.
Inductive Learning For Abductive Diagnosis 1994
Cynthia A. Thompson and Raymond J. Mooney, In Proceedings of the Twelfth National Conference on Artificial Intelligence (AAAI-94), pp. 664-669, Seattle, WA, August 1994.
Integrating ILP and EBL 1994
Raymond J. Mooney and John M. Zelle, Sigart Bulletin (special issue on Inductive Logic Programmming), Vol. 5, 1 (1994), pp. 12-21.
Learning Qualitative Models for Systems with Multiple Operating Regions 1994
Sowmya Ramachandran, Raymond J. Mooney, and Benjamin J. Kuipers, In Proceedings of the Eighth International Workshop on Qualitative Reasoning about Physical Systems, Nara, Japan 1994.
Modifying Network Architectures For Certainty-Factor Rule-Base Revision 1994
J. Jeffrey Mahoney and Raymond J. Mooney, In Proceedings of the International Symposium on Integrating Knowledge and Neural Heuristics (ISIKNH-94), pp. 75--85, Pensacola, FL, May 1994.
Multiple-Fault Diagnosis Using General Qualitative Models with Fault Modes 1994
Siddarth Subramanian and Raymond J. Mooney, In Working Papers of the Fifth International Workshop on Principles of Diagnosis, pp. 321-325, New Paltz, NY, October 1994.
Theory Refinement Combining Analytical and Empirical Methods 1994
Dirk Ourston and Raymond J. Mooney, Artificial Intelligence (1994), pp. 311-344.
Combining Connectionist and Symbolic Learning to Refine Certainty-Factor Rule-Bases 1993
J. Jeffrey Mahoney and Raymond J. Mooney, Connection Science (1993), pp. 339-364.
Combining FOIL and EBG to Speed-Up Logic Programs 1993
John M. Zelle and Raymond J. Mooney, In Proceedings of the 13th International Joint Conference on Artificial Intelligence, pp. 1106-1111 1993. San Francisco, CA: Morgan Kaufmann.
Extending Theory Refinement to M-of-N Rules 1993
Paul T. Baffes and Raymond J. Mooney, Informatica, Vol. 17 (1993), pp. 387-397.
Induction Over the Unexplained: Using Overly-General Domain Theories to Aid Concept Learning 1993
Raymond J. Mooney, Machine Learning, Vol. 10 (1993), pp. 79-110.
Inductive Learning For Abductive Diagnosis 1993
Cynthia A. Thompson, Masters Thesis, Department of Computer Sciences, The University of Texas at Austin. 53 pages.
Integrating Theory and Data in Category Learning 1993
Raymond J. Mooney, In Categorization by Humans and Machines, G. V. Nakamura and D. L. Medin and R. Taraban (Eds.), pp. 189-218 1993.
Learning Search-Control Heuristics for Logic Programs: Applications to Speedup Learning and Language Acquisition 1993
John M. Zelle, unpublished. Ph.D. proposal, Department of Computer Sciences, University of Texas at Austin.
Learning Semantic Grammars With Constructive Inductive Logic Programming 1993
John M. Zelle and Raymond J. Mooney, In Proceedings of the 11th National Conference on Artificial Intelligence, pp. 817-822 1993. Menlo Park, CA: AAAI Press.
Learning to Model Students: Using Theory Refinement to Detect Misconceptions 1993
Paul T. Baffes, unpublished. Ph.D. proposal, Department of Computer Sciences, University of Texas at Austin.
Symbolic Revision of Theories With M-of-N Rules 1993
Paul T. Baffes and Raymond J. Mooney, In Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence (IJCAI-93), pp. 1135-1140, Chambery, France, August 1993.
A First-Order Horn-Clause Abductive System and Its Use in Plan Recognition and Diagnosis 1992
Hwee Tou Ng and Raymond J. Mooney, unpublished. Unpublished Technical Note.
A General Abductive system with application to plan recognition and diagnosis 1992
Hwee Tou Ng, PhD Thesis, Department of Computer Sciences, University of Texas at Austin. 154 pages.
Abductive Plan Recognition and Diagnosis: A Comprehensive Empirical Evaluation 1992
Hwee Tou Ng and Raymond J. Mooney, In Proceedings of the Third International Conference on Principles of Knowledge Representation and Reasoning, pp. 499--508, Cambridge, MA, October 1992.
An Operator-Based Approach to First-Order Theory Revision 1992
Bradley Lance Richards, PhD Thesis, Department of Computer Science, University of Texas at Austin.
Automated Debugging of Logic Programs via Theory Revision 1992
Raymond J. Mooney and Bradley L. Richards, In Proceedings of the Second International Workshop on Inductive Logic Programming (ILP-92), Tokyo, Japan 1992.
Automatic Abduction of Qualitative Models 1992
Bradley L. Richards, Ina Kraan, and Benjamin J. Kuipers, In Proceedings of the Fifth International Workshop on Qualitative Reasoning about Physical Systems, pp. 295-301 1992.
Batch versus Incremental Theory Refinement 1992
Raymond J. Mooney, In Proceedings of the 1992 AAAI Spring Symposium on Knowledge Assimilation, Standford, CA, March 1992.
Belief Revision in the Context of Abductive Explanation 1992
Siddarth Subramanian, Technical Report AI92-179, Artificial Intelligence Laboratory, University of Texas.
Combining Symbolic and Neural Learning to Revise Probabilistic Theories 1992
J. Jeffrey Mahoney and Raymond J. Mooney, In Proceedings of the ML92 Workshop on Integrated Learning in Real Domains, Aberdeen, Scotland, July 1992.
Growing Layers of Perceptrons: Introducing the Extentron Algorithm 1992
Paul T. Baffes and John M. Zelle, In Proceedings of the 1992 International Joint Conference on Neural Networks, pp. 392--397, Baltimore, MD, June 1992.
Learning Relations by Pathfinding 1992
Bradley L. Richards and Raymond J. Mooney, In Proceedings of the Tenth National Conference on Artificial Intelligence (AAAI-92), pp. 50-55, San Jose, CA, July 1992.
Schema acquisition from a single example 1992
W. Ahn, W. F. Brewer and Raymond J. Mooney, Journal of Experimental Psychology: Learning, Memory, and Cognition, Vol. 18 (1992), pp. 391-412.
Speeding-up Logic Programs by Combining EBG and FOIL 1992
John M. Zelle and Raymond J. Mooney, In Proceedings of the 1992 Machine Learning Workshop on Knowledge Compilation and Speedup Learning, Aberdeen, Scotland, July 1992.
Using Theory Revision to Model Students and Acquire Stereotypical Errors 1992
Paul T. Baffes and Raymond J. Mooney, In Proceedings of the Fourteenth Annual Conference of the Cognitive Science Society, pp. 617-622, Bloomington, IN 1992.
An Efficient First-Order Horn-Clause Abduction System Based on the ATMS 1991
Hwee Tou Ng and Raymond J. Mooney, In Proceedings of the Ninth National Conference on Artificial Intelligence (AAAI-91), pp. 494-499, Anaheim, CA, July 1991.
Constructive Induction in Theory Refinement 1991
Raymond J. Mooney and Dirk Ourston, In Proceedings of the Eighth International Workshop on Machine Learning, pp. 178-182, Evanston, IL, June 1991.
First-Order Theory Revision 1991
Bradley L. Richards and Raymond J. Mooney, In Proceedings of the Eighth International Machine Learning Workshop, pp. pp. 447-451, Evanston, IL, June 1991.
Improving Shared Rules in Multiple Category Domain Theories 1991
Dirk Ourston and Raymond J. Mooney, In Proceedings of the Eighth International Workshop on Machine Learning, pp. 534-538, Evanston, IL, June 1991.
Symbolic and Neural Learning Algorithms: An Experimental Comparison 1991
J.W. Shavlik, Raymond J. Mooney and G. Towell, Machine Learning, Vol. 6 (1991), pp. 111-143. Reprinted in {it Readings in Knowledge Acquisition and Learning}, Bruce G. Buchanan and David C. Wilkins (eds.), Morgan Kaufman, San Mateo, CA, 19...
Theory Refinement with Noisy Data 1991
Raymond J. Mooney and Dirk Ourston, Technical Report AI91-153, Artificial Intelligence Laboratory, University of Texas.
Using Explanation-Based and Empirical Methods in Theory Revision 1991
Dirk Ourston, PhD Thesis, Department of Computer Science, University of Texas at Austin.
Changing the Rules: A Comprehensive Approach to Theory Refinement 1990
D. Ourston and Raymond J. Mooney, In Proceedings of the Eighth National Conference on Artificial Intelligence (AAAI-90), pp. 815-820, Boston, MA, July 1990.
Learning Plan Schemata From Observation: Explanation-Based Learning for Plan Recognition 1990
Raymond J. Mooney, Cognitive Science, Vol. 14, 4 (1990), pp. 483-509.
On the Role of Coherence in Abductive Explanation 1990
Hwee Tou Ng and Raymond J. Mooney, In Proceedings of the Eighth National Conference on Artificial Intelligence (AAAI-90), pp. 337--342, Boston, MA, July 1990.
An Experimental Comparison of Symbolic and Connectionist Learning Algorithms 1989
Raymond J. Mooney, J.W. Shavlik, G. Towell and A. Gove, In Proceedings of the Eleventh International Joint Conference on Artificial Intelligence (IJCAI-89), pp. 775-780, Detroit, MI, August 1989. Reprinted in ``Readings in Machine Learning'', Jude ...
Processing Issues in Comparisons of Symbolic and Connectionist Learning Systems 1989
Douglas Fisher, Kathleen McKusick, Raymond J. Mooney, Jude W. Shavlik, and Geoffrey Towell, In Proceedings of the Sixth International Workshop on Machine Learning, pp. 169--173, Ithaca, New York 1989.
The Effect of Rule Use on the Utility of Explanation-Based Learning 1989
Raymond J. Mooney, In Proceedings of the 11th International Joint Conference on Artificial Intelligence, pp. 725-730 1989. San Francisco, CA: Morgan Kaufmann.
A General Explanation-Based Learning Mechanism and its Application to Narrative Understanding 1988
Raymond J. Mooney, Ph.D. thesis, Department of Computer Science, University of Illinois at Urbana-Champaign, 1988
Generalizing the Order of Operators in Macro-Operators 1988
Raymond J. Mooney, In Proceedings of the Fifth International Conference on Machine Learning (ICML-88), pp. 270-283, Ann Arbor, MI, June 1988.
Integrated Learning of Words and their Underlying Concepts 1987
Raymond J. Mooney, In Proceedings of the Ninth Annual Conference of the Cognitive Science Society, pp. 947-978, Seattle, WA, July 1987.
Schema Acquisition from One Example: Psychological Evidence for Explanation-Based Learning 1987
W. Ahn, Raymond J. Mooney, W.F. Brewer and G.F. DeJong, In Proceedings of the Ninth Annual Conference of the Cognitive Science Society, pp. 50-57, Seattle, WA, July 1987.
A Domain Independent Explanation-Based Generalizer 1986
Raymond J. Mooney and S.W. Bennett, In Proceedings of the Fifth National Conference on Artificial Intelligence (AAAI-86), pp. 551-555, Philadelphia, PA, August 1986.
Explanation-Based Learning: An Alternative View 1986
G.F. DeJong and Raymond J. Mooney, Machine Learning (1986), pp. 145-176.
Generalizing Explanations of Narratives into Schemata 1985
Raymond J. Mooney, Masters Thesis, Department of Computer Science, University of Illinois at Urbana-Champaign.
Generalizing Explanations of Narratives into Schemata 1985
Raymond J. Mooney, In Proceedings of the Third International Machine Learning Workshop, pp. 126--128, New Brunswick, New Jersey 1985.
Learning Schemata for Natural Language Processing 1985
Raymond J. Mooney and Gerald F. DeJong, In Proceedings of the Ninth International Joint Conference on Artificial Intelligence (IJCAI-85), pp. 681-687, Los Angeles, CA, August 1985.
Areas of Interest
     [Expand to show all 39][Minimize]
Demos
Learning to Interpret Natural Language Navigation Instructions from ObservationsDavid L. Chen2012
Learning to Sportscast
Software/Data
     [Expand to show all 15][Minimize]
Improving Black-box Speech Recognition using Semantic Parsing The data used in the paper Improving Black-box Speech Recognition using Semantic Parsing, IJCNLP 2017 can be downloaded ... 2019
KRISPER A semantic parser learning system that learn from ambiguous training examples.... 2007
WASP A semantic parser learning system that uses statistical machine translation techniques. ... 2007
ACCEL ACCEL is a general purpose system that uses abductive reasoning to construct explanations for observed intelligent pheno... 2000
BETH An ILP system that integrates traditional top-down and bottom-up approaches to combine the strengths of each and elimina... 2000
CHILL CHILL (Constructive Heuristics Induction for Language Learning) is a general approach to the problem of inducing natural... 2000
DOLPHIN DOLPHIN is a system which combines Inductive Logic Programming (i.e. FOIL) and Explanation-Based Learning (i.e. EBG) to ... 2000
FOIDL FOIDL is an ILP system for learning first-order decision lists (ordered lists of clauses each ending in a cut). It has b... 2000
FORTE FORTE (First Order Revision of Theories from Examples) is a machine learning system for modifiying a first-order Horn-cl... 2000
ML Programs A set of standard inductive classification algorithms and software for automated experimentation and system comparison w... 2000
NEITHER NEITHER is a propositional theory refinement system that will modify a incomplete or incorrect rule base so as to make i... 2000
RAPIER RAPIER is a bottom-up inductive learning system for learning information extract rules. It has been tested on several do... 2000
Geoquery A natural-language system that answers questions on US Geography. Accessible at here.... 0000