Computational and Statistical Approaches to Language

The last decade has seen a dramatic increase in the number and variety of computational and statistical approaches to the acquisition and processing of language. In this graduate seminar, we will take stock of recent developments in this rapidly growing area of language research. We will survey a variety of symbolic, connectionist, and statistical language models and critically assess the degree to which these models fit the relevant psycholinguistic data and provide the bases for new empirical predictions.

The seminar is aimed at students who wish to gain insight into the modeling of psycholinguistic data using computational and statistical models, and covers aspects of language ranging from word segmentation to grammatical category classification to sentence processing. Specific topics of discussion include: What assumptions are inherent in the models in terms of representations, processing requirements, learnability considerations, etc.? How are the models to be compared with psycholinguistic data expressed in terms of reading times, grammaticality ratings/preferences, qualitative performance and/or learning profiles, etc? What are the relations between acquisition and adult processing in the models?

The first introductory meeting is Friday, January 9, 1998, at 2:00PM.

Click here to see the course syllabus.

Contact Information

Morten H. Christiansen
Program in Neural, Informational and Behavioral Sciences
University of Southern California
Office: Hedco Neurosciences Bldg. B11
Phone: (213) 740-6299
Fax: (213) 740-5687
Email: morten@gizmo.usc.edu

Introduction

Pinker, S. (1979). Formal Models of Language Learning. Cognition, 7, 217-283.

Word Segmentation

Saffran, J. R., Newport, E. L., & Aslin, R. N. (1996). Word Segmentation: The Role of Distributional Cues. Journal of Memory and Language, 35, 606-621.

Cairns, P., Shillcock, R., Chater, N. & Levy, J. (1997). Bootstrapping Word Boundaries: A Bottom-up Corpus-based Approach to Speech Segmentation. Cognitive Psychology, 33, 111-153.

Brent, M.R. & Cartwright, T.A. (1996). Distributional Regularity and Phonotactics are Useful for Segmentation. Cognition, 61, 93-125.

Dahan, D. & Brent, M.R. (submitted). On the Discovery of Novel Word-like Units from Utterances: An Artificial-language study with Implications for Native-Language Acquisition.

Word Learning

Siskind, J.M. (1996). A Computational Study of Cross-situational Techniques for Learning Word-to-meaning Mappings. Cognition, 61, 39-91.

Golinkoff, R.M., Hirsh-Pasek, K. & Hollich, G. (in press). Emerging Cues for Early Word Learning. To appear in B. MacWhinney (Ed.), Emergentist Approaches to Language. Mahwah, NJ: Lawrence Erlbaum Associates.

Landauer, T.K. & Dumais, S.T. (1997). A Solution to Plato's Problem: The Latent Semantic Analysis Theory of Acquisition, Induction, and Representation of Knowledge. Psychological Review, 104, 211-240.

Induction of Grammatical Knowledge

Cartwright, T.A. & Brent, M.R. (1997). Syntactic Categorization in Early Language Acquisition - Formalizing the Role of Distributional Analysis. Cognition, 63, 121-170.

Redington, M., Chater, N. & Finch, S. (in press). The Potential Contribution of Distributional Information to Early Syntactic Category Acquisition. Cognitive Science.

Jurafsky, D. (1996). A Probabilistic Model of Lexical and Syntactic Access and Disambiguation. Cognitive Science, 20, 137-194.

Fisher, C. & Tokura, H. (1996). Acoustic Cues to Grammatical Structure in Infant-directed Speech - Cross-linguistic Evidence. Child Development, 67, 3192-3218.

Gibson, E. & Wexler, K. (1994). Triggers. Linguistic Inquiry, 25, 407-454.

Niyogi, P. & Berwick, R.C. (1996). A Language Learning Model for Finite Parameter Spaces. Cognition, 61, 161-193.

Processing of Syntactic Structure

Gibson, E. (in press). Linguistic Complexity: Locality of Syntactic Dependencies. Cognition.

Lewis, R.L. & Lehman, J.F. (1997). Comprehending with Limited Resources: A Theory of Sentence Processing Architecture. Unpublished Manuscript, Department of Computer and Information Science, Ohio State University.

Just, M.A. & Carpenter, P.A. (1992). A Capacity Theory of Comprehension: Individual Differences in Working Memory. Psychological Review, 99, 122-149.

MacDonald, M. & Christiansen, M.H. (submitted). Individual Differences without Working Memory: A Reply to Just & Carpenter and Waters & Caplan.

Tabor, W. & Tanenhaus, M.K. (submitted). Dynamical Models of Sentence Processing.

Burgess, C. & Lund, K. (1997). Modeling Parsing Constraints with High-dimensional Context Space. Language and Cognitive Processes, 12, 177-210.

Reading

Spieler, D.H. & Balota, D.A. (1997). Bringing Computational Models of Word Naming Down to the Item Level. Psychological Science, 8, 411-416.

Seidenberg, M.S. & Plaut, D.C. (in press). Evaluating Word Reading Models at the Item Level: Matching the Grain of Theory and Data. Psychological Science.

Balota, D.A. & Spieler, D.H. (submitted). The Utility of Item-Level Analyses in Model Evaluation: A Reply to Seidenberg and Plaut (1998).

Computational and Statistical Models in Perspective

Smolensky, P. (submitted). Grammar-based Connectionist Approaches to Language.

Steedman, M. (submitted). Connectionist Sentence Processing in Perspective.

Seidenberg, M.S. (submitted). Language Processing and Language Acquisition.

Redington, M. & Chater, N. (in press). Probabilistic and Distributional Approaches to Language Acquisition. Trends in the Cognitive Sciences.

Last modified: January 5, 1998.

Home | People | Research | Links | Contact | Publications | Presentations