Publications: Cognitive Science

1. Latent Variable Models of Distributional Lexical Semantics
   [Details] [PDF]
   Joseph Reisinger
   PhD Thesis, Department of Computer Science, University of Texas at Austin, May 2012.

   In order to respond to increasing demand for natural language interfaces—and provide meaningful insight into user query intent—fast, scalable lexical semantic models with flexible representations are needed. Human concept organization is a rich phenomenon that has yet to be accounted for by a single coherent psychological framework: Concept generalization is captured by a mixture of prototype and exemplar models, and local taxonomic information is available through multiple overlapping organizational systems. Previous work in computational linguistics on extracting lexical semantic information from unannotated corpora does not provide adequate representational flexibility and hence fails to capture the full extent of human conceptual knowledge. In this thesis I outline a family of probabilistic models capable of capturing important aspects of the rich organizational structure found in human language that can predict contextual variation, selectional preference and feature-saliency norms to a much higher degree of accuracy than previous approaches. These models account for cross-cutting structure of concept organization—i.e. selective attention, or the notion that humans make use of different categorization systems for different kinds of generalization tasks—and can be applied to Web-scale corpora. Using these models, natural language systems will be able to infer a more comprehensive semantic relations, which in turn may yield improved systems for question answering, text classification, machine translation, and information retrieval.

   ML ID: 309

2. Integrating Theory and Data in Category Learning
   [Details] [PDF]
   Raymond J. Mooney
   In G. V. Nakamura and D. L. Medin and R. Taraban, editors, Categorization by Humans and Machines, 189-218, 1993.

   Recent results in both machine learning and cognitive psychology demonstrate that effective category learning involves an integration of theory and data. First, theories can bias induction, affecting what category definitions are extracted from a set of examples. Second, conflicting data can cause theories to be revised. Third, theories can alter the representation of data through feature formation. This chapter reviews two machine learning systems that attempt to integrate theory and data in one or more of these ways. IOU uses a domain theory to acquire part of a concept definition and to focus induction on the unexplained aspects of the data. EITHER uses data to revise an imperfect theory and uses theory to add abstract features to the data. Recent psychological experiments reveal that these machine learning systems exhibit several important aspects of human category learning. Specifically, IOU has been used to successfully model some recent experimental results on the effect of functional knowledge on category learning.

   ML ID: 22

3. Induction Over the Unexplained: Using Overly-General Domain Theories to Aid Concept Learning
   [Details] [PDF]
   Raymond J. Mooney
   Machine Learning, 10:79-110, 1993.

   This paper describes and evaluates an approach to combining empirical and explanation-based learning called Induction Over the Unexplained (IOU). IOU is intended for learning concepts that can be partially explained by an overly-general domain theory. An eclectic evaluation of the method is presented which includes results from all three major approaches: empirical, theoretical, and psychological. Empirical results shows that IOU is effective at refining overly-general domain theories and that it learns more accurate concepts from fewer examples than a purely empirical approach. The application of theoretical results from PAC learnability theory explains why IOU requires fewer examples. IOU is also shown to be able to model psychological data demonstrating the effect of background knowledge on human learning.

   ML ID: 20

4. Schema acquisition from a single example
   [Details] [PDF]
   W. Ahn, W. F. Brewer and Raymond J. Mooney
   Journal of Experimental Psychology: Learning, Memory, and Cognition, 18:391-412, 1992.

   This study compares similarity-based learning (SBL) and explanation-based learning (EBL) approaches to schema acquisition. In SBL approaches, concept formation is based on similarity across multiple examples. However, these approaches seem to be appropriate when the learner cannot apply existing knowledge and when the concepts to be learned are nonexplanatory. EBL approaches assume that a schema can be acquired from even a single example by constructing an explanation of the example using background knowledge, and generalizing the resulting explanation. However, unlike the current EBL theories, Exp 1 showed significant EBL occurred only when the background information learned during the experiment was actively used by the Ss. Exp 2 showed the generality of EBL mechanisms across a variety of materials and test procedures.

   ML ID: 212

5. Learning Plan Schemata From Observation: Explanation-Based Learning for Plan Recognition
   [Details] [PDF]
   Raymond J. Mooney
   Cognitive Science, 14(4):483-509, 1990.

   This article discusses how explanation-based learning of plan schemata from observation can improve performance of plan recognition. The GENESIS program is presented as an implemented system for narrative text understanding that learns schemata and improves its performance. Learned schemata allow GENESIS to use schema-based understanding techniques when interpreting events and thereby avoid the expensive search associated with plan-based understanding. Learned schemata also function as new concepts that can be used to cluster examples and index events in memory. In addition. experiments are reviewed which demonstrate that human subjects, like GENESIS, can learn a schema by observing, explaining, and generalizing a single specific instance presented in a narrative.

   ML ID: 1

6. Integrated Learning of Words and their Underlying Concepts
   [Details] [PDF]
   Raymond J. Mooney
   In Proceedings of the Ninth Annual Conference of the Cognitive Science Society, 947-978, Seattle, WA, July 1987.

   Models of learning word meanings have generally assumed prior knowledge of the concepts to which the words refer. However, novel natural language text or discourse often presents both unknown concepts and words which refer to these concepts. Also, developmental data suggests that the learning of words and their concepts frequently occurs concurrently instead of concept learning proceeding word learning. This paper presents an integrated computational model for acquiring both word meanings and their underlying concepts concurrently. This model is implemented as a word learning component added to the GENESIS explanation-based learning schema acquisition system for narrative understanding. A detailed example is described in which GENESIS learns provisional definitions for the words "kidnap", "kidnapper", and "ransom" as well as a kidnapping schema from a single narrative.

   ML ID: 208

7. Schema Acquisition from One Example: Psychological Evidence for Explanation-Based Learning
   [Details] [PDF]
   W. Ahn, Raymond J. Mooney, W.F. Brewer and G.F. DeJong
   In Proceedings of the Ninth Annual Conference of the Cognitive Science Society, 50-57, Seattle, WA, July 1987.

   Recent explanation-based learning (EBL) models in AI allow a computer program to learn a schema by analyzing a single example. For example, GENESIS is an EBL system which learns a plan schema from a single specific instance presented in a narrative. Previous learning models in both AI and psychology have required multiple examples. This paper presents experimental evidence that people can learn a plan schema from a single narrative and that the learned schema agrees with that predicted by EBL. This evidence suggests that GENESIS, originally constructed as a machine learning system, can be interpreted as a psychological model of learning a complex schema from a single example.

   ML ID: 207