Publications: 2002
- Property-Based Feature Engineering and Selection
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Noppadon Kamolvilassatian
Masters Thesis, Department of Computer Sciences, University of Texas at Austin, Austin, TX, December 2002. 85 pages.Example representation is a fundamental problem in machine learning. In particular, the decision on what features are extracted and selected to be included in the learning process significantly affects learning performance.
This work proposes a novel framework for feature representation based on feature properties and applies it to the domain of textual information extraction. Our framework enables knowledge on feature engineering and selection to be explicitly learned and applied. The application of this knowledge can improve learning performance within the domain from which it is learned and in other domains with similar representational bias.
We conducted several experiments comparing the performance of feature engineering and selection methods based on our framework with other approaches in the Information Extraction task. Results suggested that our approach performs either competitively or better than the best heuristic-based feature selection approach used. Moreover, our general framework can potentially be combined with other feature selection approaches to yield even better results.
ML ID: 118
- Mining Soft-Matching Association Rules
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Un Yong Nahm and Raymond J. Mooney
In Proceedings of the Eleventh International Conference on Information and Knowledge Management (CIKM-2002), 681-683, McLean, VA, November 2002.Variation and noise in database entries can prevent data mining algorithms, such as association rule mining, from discovering important regularities. In particular, textual fields can exhibit variation due to typographical errors, mispellings, abbreviations, etc.. By allowing partial or "soft matching" of items based on a similarity metric such as edit-distance or cosine similarity, additional important patterns can be detected. This paper introduces an algorithm, SoftApriori that discovers soft-matching association rules given a user-supplied similarity metric for each field. Experimental results on several "noisy" datasets extracted from text demonstrate that SoftApriori discovers additional relationships that more accurately reflect regularities in the data.
ML ID: 117
- Relational Data Mining with Inductive Logic Programming for Link Discovery
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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.Link discovery (LD) is an important task in data mining for counter-terrorism and is the focus of DARPA's Evidence Extraction and Link Discovery (EELD) research program. Link discovery concerns the identification of complex relational patterns that indicate potentially threatening activities in large amounts of relational data. Most data-mining methods assume data is in the form of a feature-vector (a single relational table) and cannot handle multi-relational data. Inductive logic programming is a form of relational data mining that discovers rules in first-order logic from multi-relational data. This paper discusses the application of ILP to learning patterns for link discovery.
ML ID: 116
- Two Approaches to Handling Noisy Variation in Text Mining
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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, 18-27, Sydney, Australia, July 2002.Variation and noise in textual database entries can prevent text mining algorithms from discovering important regularities. We present two novel methods to cope with this problem: (1) an adaptive approach to ``hardening'' noisy databases by identifying duplicate records, and (2) mining ``soft'' association rules. For identifying approximately duplicate records, we present a domain-independent two-level method for improving duplicate detection accuracy based on machine learning. For mining soft matching rules, we introduce an algorithm that discovers association rules by allowing partial matching of items based on a textual similarity metric such as edit distance or cosine similarity. Experimental results on real and synthetic datasets show that our methods outperform traditional techniques for noisy textual databases.
ML ID: 115
- Content-Boosted Collaborative Filtering for Improved Recommendations
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Prem Melville, Raymond J. Mooney, and Ramadass Nagarajan
In Proceedings of the Eighteenth National Conference on Artificial Intelligence (AAAI-02), 187-192, Edmonton, Alberta, 2002.Most recommender systems use Collaborative Filtering or Content-based methods to predict new items of interest for a user. While both methods have their own advantages, individually they fail to provide good recommendations in many situations. Incorporating components from both methods, a hybrid recommender system can overcome these shortcomings. In this paper, we present an elegant and effective framework for combining content and collaboration. Our approach uses a content-based predictor to enhance existing user data, and then provides personalized suggestions through collaborative filtering. We present experimental results that show how this approach, Content-Boosted Collaborative Filtering, performs better than a pure content-based predictor, pure collaborative filter, and a naive hybrid approach.
ML ID: 114
- Semi-supervised Clustering by Seeding
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Sugato Basu, Arindam Banerjee, and Raymond J. Mooney
In Proceedings of 19th International Conference on Machine Learning (ICML-2002), 19-26, 2002.Semi-supervised clustering uses a small amount of labeled data to aid and bias the clustering of unlabeled data. This paper explores the use of labeled data to generate initial seed clusters, as well as the use of constraints generated from labeled data to guide the clustering process. It introduces two semi-supervised variants of KMeans clustering that can be viewed as instances of the EM algorithm, where labeled data provides prior information about the conditional distributions of hidden category labels. Experimental results demonstrate the advantages of these methods over standard random seeding and COP-KMeans, a previously developed semi-supervised clustering algorithm.
ML ID: 113
- Text Mining with Information Extraction
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Un Yong Nahm and Raymond J. Mooney
In Proceedings of the AAAI 2002 Spring Symposium on Mining Answers from Texts and Knowledge Bases, 60-67, Stanford, CA, March 2002.Text mining concerns looking for patterns in unstructured text. The related task of Information Extraction (IE) is about locating specific items in natural-language documents. This paper presents a framework for text mining, called DiscoTEX (Discovery from Text EXtraction), using a learned information extraction system to transform text into more structured data which is then mined for interesting relationships. The initial version of DiscoTEX integrates an IE module acquired by an IE learning system, and a standard rule induction module. However, this approach has problems when the same extracted entity or feature is represented by similar but not identical strings in different documents. Consequently, we also develop an alternate rule induction system called TextRISE, that allows for partial matching of textual items. Encouraging preliminary results are presented on applying these techniques to a corpus of Internet documents.
ML ID: 112
- Extracting Gene and Protein Names from Biomedical Abstracts
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Razvan Bunescu, Ruifang Ge, Raymond J. Mooney, Edward Marcotte, and Arun Kumar Ramani
March 2002. Unpublished Technical Note.Automatically extracting information from biomedical text holds the promise of easily consolidating large amounts of biological knowledge in computer accessible form. We are investigating the use of information extraction techniques for processing biomedical text. Currently, we have focused on the initial stage of identifying information on interacting proteins, specifically the problem of recognizin protein and gene names with high precision. We present preliminary results on extracting protein names from Medline abstracts.
ML ID: 111
- Learning to Combine Trained Distance Metrics for Duplicate Detection in Databases
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Mikhail Bilenko and Raymond J. Mooney
Technical Report AI 02-296, Artificial Intelligence Laboratory, University of Texas at Austin, Austin, TX, February 2002.The problem of identifying approximately duplicate records in databases has previously been studied as record linkage, the merge/purge problem, hardening soft databases, and field matching. Most existing approaches have focused on efficient algorithms for locating potential duplicates rather than precise similarity metrics for comparing records. In this paper, we present a domain-independent method for improving duplicate detection accuracy using machine learning. First, trainable distance metrics are learned for each field, adapting to the specific notion of similarity that is appropriate for the field's domain. Second, a classifier is employed that uses several diverse metrics for each field as distance features and classifies pairs of records as duplicates or non-duplicates. We also propose an extended model of learnable string distance which improves over an existing approach. Experimental results on real and synthetic datasets show that our method outperforms traditional techniques.
ML ID: 110
