Extracting semantic representations from word co-occurrence statistics: Stop-lists, stemming, and SVD

Behavior Research Methods

Volumn 44, Issue 3, 2012, Pages 890-907

  Bullinaria, John A ^a   Levy, Joseph P ^b  

^a UNIVERSITY OF BIRMINGHAM   (United Kingdom)

^b UNIVERSITY OF ROEHAMPTON   (United Kingdom)

Author keywords

Corpus statistics; Semantic representation; SVD

Indexed keywords

ARTICLE; ARTIFICIAL INTELLIGENCE; COMPUTER PROGRAM; DECISION MAKING; HUMAN; LINGUISTICS; SEMANTICS; STATISTICAL ANALYSIS;

ARTIFICIAL INTELLIGENCE; CHOICE BEHAVIOR; DATA INTERPRETATION, STATISTICAL; HUMANS; JUDGMENT; PSYCHOLINGUISTICS; SEMANTICS; SOFTWARE;

EID: 84865654632     PISSN: 1554351X     EISSN: 15543528     Source Type: Journal    
DOI: 10.3758/s13428-011-0183-8     Document Type: Article

References (35)

1. Aston, G., & Burnard, L. (1998). The BNC handbook: Exploring the British National Corpus with SARA. Edinburgh: Edinburgh University Press.
2. Baroni, M., Bernardini, S., Ferraresi, A., & Zanchetta, E. (2009). The WaCky wide Web: A collection of very large linguistically processed Web-crawled corpora. Language Resources and Evaluation, 43, 209-226. [Corpus Web site: http://wacky. sslmit. unibo. it/].
3. Baroni, M., & Lenci, A. (2010). Distributional memory: A general framework for corpus-based semantics. Computational Linguistics, 36, 673-721.
4. 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 Monograph, 80, 1-45.
5. Bullinaria, J. A. (2008). Semantic categorization using simple word co-occurrence statistics. In M. Baroni, S. Evert, & A. Lenci (Eds.), Proceedings of the ESSLLI Workshop on Distributional Lexical Semantics (pp. 1-8). Hamburg, Germany: ESSLLI.
6. Bullinaria, J. A., & Levy, J. P. (2007). Extracting semantic representations from word co-occurrence statistics: A computational study. Behavior Research Methods, 39, 510-526.
7. Burnham, K. P., & Anderson, D. R. (2010). Model selection and multi-model inference: A practical information-theoretic approach. Berlin: Springer.
8. Caron, J. (2001). Experiments with LSA scoring: Optimal rank and basis. In M. W. Berry (Ed.), Computational information retrieval (pp. 157-169). Philadelphia, PA: SIAM.
9. Finch, S. P., & Chater, N. (1992). Bootstrapping syntactic categories. In Proceedings of the Fourteenth Annual Conference of the Cognitive Science Society (pp. 820-825). Hillsdale, NJ: Erlbaum.
10. French, R. M., & Labiouse, C. (2002). Four problems with extracting human semantics from large text corpora. In Proceedings of the Twenty-fourth Annual Conference of the Cognitive Science Society (pp. 316-322). Mahwah, NJ: Erlbaum.
11. Griffiths, T. L., Steyvers, M., & Tenenbaum, J. B. (2007). Topics in semantic representation. Psychological Review, 114, 211-244.
12. Honkela, T., Hyvärinen, A., & Väyrynen, J. J. (2010). WordICA-Emergence of linguistic representations for words by independent component analysis. Natural Language Engineering, 16, 277-308.
13. Jones, M. N., & Mewhort, D. J. K. (2007). Representing word meaning and order information in a composite holographic lexicon. Psychological Review, 114, 1-37.
14. Karypis, G. (2003). CLUTO: A clustering toolkit (Release 2. 1. 1) (Tech. Rep. 02-017). Minneapolis: University of Minnesota, Department of Computer Science. Available from the CLUTO Web site: http://glaros. dtc. umn. edu/gkhome/views/cluto.
15. 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.
16. Landauer, T. K., & Dumais, S. T. (2008). Latent semantic analysis. Scholarpedia, 3, 4356.
17. Landauer, T. K., McNamara, D. S., Dennis, S., & Kintsch, W. (2007). Handbook of latent semantic analysis. Mahwah, NJ: Erlbaum.
18. 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. Sougné (Eds.), Connectionist models of learning, development and evolution (pp. 273-282). London: Springer.
19. Levy, J. P., & Bullinaria, J. A. (2012). Using enriched semantic representations in predictions of human brain activity. In E. J. Davelaar (Ed.), Connectionist models of neurocognition and emergent behavior: From theory to applications (pp. 292-308). Singapore: World Scientific.
20. 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.
21. Lund, K., & Burgess, C. (1996). Producing high-dimensional semantic spaces from lexical co-occurrence. Behavior Research Methods, Instruments, & Computers, 28, 203-208.
22. Manning, C. D., & Schütze, H. (1999). Foundations of statistical natural language processing. Cambridge, MA: MIT Press.
23. Matveeva, I., Levow, G., Farahat, A., & Royer, C. (2005). Generalized latent semantic analysis for term representation. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP-05). Borovets, Bulgaria.
24. Mitchell, T. M., Shinkareva, S. V., Carlson, A., Chang, K.-M., Malave, V. L., Mason, R. A., & Just, M. A. (2008). Predicting human brain activity associated with the meanings of nouns. Science, 320, 1191-1195.
25. Pado, S., & Lapata, M. (2007). Dependency-based construction of semantic space models. Computational Linguistics, 33, 161-199.
26. 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.
27. Porter, M. F. (1980). An algorithm for suffix stripping. Program, 14, 130-137.
28. Rapp, R. (2003). Word sense discovery based on sense descriptor dissimilarity. In Proceedings of the Ninth Machine Translation Summit (pp. 315-322). New Orleans, LA.
29. Schütze, H. (1993). Word space. In S. J. Hanson, J. D. Cowan, & C. L. Giles (Eds.), Advances in neural information processing systems 5 (pp. 895-902). San Mateo, CA: Morgan Kauffmann.
30. Terra, E., & Clarke, C. L. A. (2003). Frequency estimates for statistical word similarity measures. In Proceedings of the Human Language Technology and North American Chapter of Association of Computational Linguistics Conference 2003 (HLT/NAACL 2003) (pp. 244-251). Edmonton, AB, Canada.
31. Turney, P. D. (2001). Mining the Web for synonyms: PMI-IR versus LSA on TOEFL. In Proceedings of the Twelfth European Conference on Machine Learning (ECML-2001) (pp. 491-502). Freiburg, Germany.
32. Turney, P. D. (2008). A uniform approach to analogies, synonyms, antonyms, and associations. In Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008) (pp. 905-912). Manchester, U. K.
33. Turney, P. D., Littman, M. L., Bigham, J., & Shnayder, V. (2003). Combining independent modules to solve multiple-choice synonym and analogy problems. Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP-03) (pp. 482-489). Borovets, Bulgaria.
34. Zhao, X., Li, P., & Kohonen, T. (2011). Contextual self-organizing map: Software for constructing semantic representation. Behavior Research Methods, 43, 77-88.
35. Zhao, Y., & Karypis, G. (2001). Criterion functions for document clustering: Experiments and analysis (Tech. Rep. TR 01-40), Minneapolis: University of Minnesota, Department of Computer Science. Available at: http://cs. umn. edu/karypis/publications.