Extracting semantic representations from word co-occurrence statistics: A computational study (original) (raw)
References
Aston, G., &Burnard, L. (1998).The BNC handbook: Exploring the British National Corpus with SARA Edinburgh: Edinburgh University Press. Google Scholar
Audet, C., &Burgess, C. (1999). Using a high-dimensional memory model to evaluate the properties of abstract and concrete words.Proceedings of the Twenty-First Annual Conference of the Cognitive Science Society (pp. 37–42). Mahwah, NJ: Erlbaum. Google Scholar
Battig, W. F., &Montague, W. E. (1969). Category norms for verbal items in 56 categories: A replication and extension of the Connecticut category norms.Journal of Experimental Psychology,80(3, Pt. 2), 1–46. Article Google Scholar
Bishop, C. M. (1995).Neural networks for pattern recognition. Oxford: Oxford University Press. Google Scholar
Bullinaria, J. A., &Huckle, C. C. (1997). Modelling lexical decision using corpus derived semantic representations in a connectionist network. In J. A. Bullinaria, D. W. Glasspool, & G. Houghton (Eds.),Fourth Neural Computation and Psychology Workshop: Connectionist Representations (pp. 213–226). London: Springer. Google Scholar
Burgess, C. (2000). Theory and operational definitions in computational memory models: A response to Glenberg and Robertson.Journal of Memory & Language,43, 402–408. Article Google Scholar
Burgess, C. (2001). Representing and resolving semantic ambiguity: A contribution from high-dimensional memory modeling. In D. S. Gorfein (Ed.),On the consequences of meaning selection: Perspectives on resolving lexical ambiguity. Washington, DC: American Psychological Association. Google Scholar
Burgess, C., &Conley, P. (1999). Representing proper names and objects in a common semantic space: A computational model.Brain & Cognition,40, 67–70. Google Scholar
Christiansen, M. H., Allen, J., &Seidenberg, M. S. (1998). Learning to segment speech using multiple cues: A connectionist model.Language & Cognitive Processes,13, 221–268. Article Google Scholar
Church, K. W., &Hanks, P. (1990). Word association norms, mutual information and lexicography.Computational Linguistics,16, 22–29. Google Scholar
Conley, P., Burgess, C., &Glosser, G. (2001). Age and Alzheimer’s: A computational model of changes in representation.Brain & Cognition,46, 86–90. Article Google Scholar
Deerwester, S., Dumais, S. T., Furnas, G. W., Landauer, T. K., &Harshman, R. (1990). Indexing by Latent Semantic Analysis.Journal of the American Society for Information Science,41(6), 391–407. Article Google Scholar
Denhière, G., &Lemaire, B. (2004). A computational model of children’s semantic memory. In_Proceedings Twenty-sixth Annual Meeting of the Cognitive Science Society_ (pp. 297–302). Mahwah, NJ: Erlbaum. Google Scholar
Finch, S. P., &Chater, N. (1992). Bootstrapping syntactic categories. In_Proceedings of the Fourteenth Annual Conference of the Cognitive Science Society of America_ (pp. 820–825). Hillsdale, NJ: Erlbaum. Google Scholar
Firth, J. R. (1957) A synopsis of linguistic theory 1930–1955. In_Studies in linguistic analysis_ (pp. 1–32). Oxford: Philological Society. [Reprinted in F. R. Palmer (Ed.) (1968).Selected papers of J. R. Firth 1952–1959. London: Longman.] Google Scholar
French, R. M., &Labiouse, C. (2002). Four problems with extracting human semantics from large text corpora.Proceedings of the Twenty-Fourth Annual Conference of the Cognitive Science Society (pp. 316–322). Mahwah, NJ: Erlbaum. Google Scholar
Glenberg, A. M., &Robertson, D. A. (2000). Symbol grounding and meaning: A comparison of high-dimensional and embodied theories of meaning,Journal of Memory & Language,43, 379–401. Article Google Scholar
Harnad, S. (1990). The symbol grounding problem.Physica D,42, 335–346. Article Google Scholar
Haykin, S. (1999).Neural networks: A comprehensive foundation (2nd ed.). Upper Saddle River, NJ: Prentice Hall. Google Scholar
Hofmann, T. (2001). Unsupervised learning by probabilistic latent semantic analysis.Machine Learning Journal,42, 177–196 Article Google Scholar
Hu, X., Cai, Z., Franceschetti, D., Graesser, A. C., &Ventura, M. (2005). Similarity between semantic spaces. In_Proceedings of the Twenty-Seventh Annual Conference of the Cognitive Science Society_ (pp. 995–1000). Mahwah, NJ: Erlbaum. Google Scholar
Hu, X., Cai, Z., Franceschetti, D., Penumatsa, P., Graesser, A. C., Louwerse, M. M., McNamara, D. S., & TRG (2003). LSA: The first dimension and dimensional weighting. In_Proceedings of the Twenty-Fifth Annual Conference of the Cognitive Science Society_ (pp. 1–6). Mahwah, NJ: Erlbaum. Google Scholar
Kintsch, W. (2000). Metaphor comprehension: A computational theory.Psychonomic Bulletin & Review,7, 257–266. Article Google Scholar
Kintsch, W., &Bowles, A. R. (2002). Metaphor comprehension: What makes a metaphor difficult to understand?Metaphor & Symbol,17, 249–262. Article Google Scholar
Kohonen, T. (1997).Self-organizing maps (2nd ed.). Berlin: Springer. Google Scholar
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. Article Google Scholar
Letsche, T. A., &Berry, M. W. (1997). Large-scale information retrieval with Latent Semantic Indexing.Information Sciences—Applications,100, 105–137. Article Google Scholar
Levy, J. P., &Bullinaria, J. A. (2001). Learning lexical properties from word usage patterns: Which context words should be used? In R. F. French & J. P. Sougne (Eds.),Connectionist models of learning, development and evolution: Proceedings of the Sixth Neural Computation and Psychology Workshop (pp. 273–282). London: Springer. Google Scholar
Levy, J. P., Bullinaria, J. A., &Patel, M. (1998). Explorations in the derivation of semantic representations from word co-occurrence statistics.South Pacific Journal of Psychology,10, 99–111. Google Scholar
Lowe, W. (2001). Towards a theory of semantic space. In_Proceedings of the Twenty-Third Annual Conference of the Cognitive Science Society_ (pp. 576–581). Mahwah, NJ: Erlbaum. Google Scholar
Lowe, W., &McDonald, S. (2000). The direct route: Mediated priming in semantic space.Proceedings of the Twenty-Second Annual Conference of the Cognitive Science Society (pp. 806–811). Mahwah, NJ: Erlbaum. Google Scholar
Lund, K., &Burgess, C. (1996). Producing high-dimensional semantic spaces from lexical co-occurrence.Behavior Research Methods, Instruments, & Computers,28, 203–208. Article Google Scholar
Manning, C. D., &Schütze, H. (1999).Foundations of statistical natural language processing. Cambridge, MA: MIT Press. Google Scholar
McDonald, S., &Lowe, W. (1998). Modelling functional priming and the associative boost. In_Proceedings of the Twentieth Annual Conference of the Cognitive Science Society_ (pp. 675–680). Mahwah, NJ: Erlbaum. Google Scholar
McDonald, S. A., &Shillcock, R. C. (2001). Rethinking the word frequency effect: The neglected role of distributional information in lexical processing.Language & Speech,44, 295–323. Article Google Scholar
Miller, T. (2003). Essay assessment with latent semantic analysis.Journal of Educational Computing Research,28, 2003. Google Scholar
Monaghan, P., Chater, N., &Christiansen, M. H. (2005). The differential role of phonological and distributional cues in grammatical categorization,Cognition,96, 143–182. ArticlePubMed Google Scholar
O’Reilly, R. C. (1998). Six principles for biologically-based computational models of cortical cognition.Trends in Cognitive Sciences,2, 455–462. ArticlePubMed Google Scholar
Patel, M., Bullinaria, J. A., &Levy, J. P. (1997). Extracting semantic representations from large text corpora. In J. A. Bullinaria, D. W. Glasspool, & G. Houghton (Eds.),Fourth Neural Computation and Psychology Workshop: Connectionist Representations (pp. 199–212). London: Springer. Google Scholar
Redington, M., Chater, N., &Finch, S. (1998). Distributional information: A powerful cue for acquiring syntactic categories,Cognitive Science,22, 425–469. Article Google Scholar
Saussure, F. de (1916).Cours de linguistique générale. Paris: Payot. Google Scholar
Schütze, H. (1993). Word space. In S. J. Hanson, J. D. Cowan, & C. L. Giles (Eds.),Advances in neural information processing systems (Vol. 5, pp. 895–902). San Mateo, CA: Morgan Kauffmann. Google Scholar
Schütze, H. (1998). Automatic word sense discrimination,Computational Linguistics,24, 97–123. Google Scholar
Turney, P. D. (2001). Mining the Web for synonyms: PMI-IR versus LSA on TOEFL. In L. De Raedt & P. A. Flach (Eds.),Proceedings of the Twelfth European Conference on Machine Learning (ECML-2001) (pp. 491–502). Berlin: Springer. Google Scholar
Wolfe, M. B. W., &Goldman, S. R. (2003). Use of Latent Semantic Analysis for predicting psychological phenomena: Two issues and proposed solutions.Behavior Research Methods, Instruments, & Computers,35, 22–31. Article Google Scholar
Zhu, H. (1997). Bayesian geometric theory of learning algorithms. In_Proceedings of the International Conference on Neural Networks (ICNN ’97)_,2, 1041–1044.