Using context to build semantics
Tóm tắt
Latent semantic analysis (LSA) is a model of knowledge representation for words. It works by applying dimension reduction to local co-occurrence data from a large collection of documents after performing singular value decomposition on it. When the reduction is applied, the system forms condensed representations for the words that incorporate higher order associations. The higher order associations are primarily responsible for any semantic similarity between words in LSA. In this article, a memory model is described that creates semantic representations for words that are similar in form to those created by LSA. However, instead of applying dimension reduction, the model builds the representations by using a retrieval mechanism from a well-known account of episodic memory.
Tài liệu tham khảo
Burgess, C., Livesay, K., &Lund, K. (1998). Explorations in context space: Words, sentences, discourse.Discourse Processes,25, 211–257.
Dennis, S., &Humphreys, M. S. (2001). A context noise model of episodic word recognition.Psychological Review,108, 452–478.
Dougherty, M. R. P., Gettys, C. F., &Ogden, E. E. (1999). MINERVA-DM: A memory process model for judgments of likelihood.Psychological Review,106, 180–209.
Dumais, S. T. (1994). Latent semantic indexing (LSI) and TREC-2. In D. K. Harman (Ed.),The Second Text Retrieval Conference (TREC-2) (Special Publication 500-215, pp. 105–116). Washington, DC: Department of Commerce, National Institute of Standards and Technology.
Dumais, S. T., &Nielsen, J. (1992). Automating the assignment of submitted manuscripts to reviewers. In N. Belkin, P. Ingwersen, & A. M. Pejtersen (Eds.),SIGIR’92: Proceedings of the 15th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 233–244). New York: ACM Press.
Hintzman, D. L. (1984). MINERVA 2: A simulation model of human memory.Behavior Research Methods, Instruments, & Computers,16, 96–101.
Hintzman, D. L. (1986). “Schema abstraction” in a multiple-trace model.Psychological Review,93, 411–428.
Hintzman, D. L. (1988). Judgments of frequency and recognition in a multiple-trace memory model.Psychological Review,95, 528–551.
Kwantes, P. J., &Mewhort, D. J. K. (1999). Modeling lexical decision and word naming as a retrieval process.Canadian Journal of Experimental Psychology,53, 306–315.
Landauer, T. K., &Dumais, S. T. (1997). A solution to Plato’s problem: The latent semantic analysis theory of the acquisition, induction, and representation of knowledge.Psychological Review,104, 211–240.
Landauer, T. K., Foltz, P. W., &Laham, D. (1998). An introduction to latent semantic analysis.Discourse Processes,25, 259–284.
Nelson, D. L., McEvoy, C. L., & Schreiber, T. A. (1999).The University of Florida word association norms. Available at www.usf.edu/FreeAssociation.
Steyvers, M., Shiffrin, R. M., &Nelson, D. L. (2005). Word association spaces for predicting semantic similarity effects in episodic memory. In A. F. Healy (Ed.),Experimental cognitive psychology and its applications (pp. 237–249). Washington, DC: American Psychological Association.