Vector Space Models of Word Meaning and Phrase Meaning: A Survey

Linguistics and Language Compass

Volumn 6, Issue 10, 2012, Pages 635-653

  Erk, Katrin ^a  

^a UNIVERSITY OF TEXAS AT AUSTIN   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84867298859     PISSN: None     EISSN: 1749818X     Source Type: Journal    
DOI: 10.1002/lnco.362     Document Type: Article

References (90)

1. Almuhareb, A., and M. Poesio 2004. Attribute-based and value-based clustering: an evaluation. Proceedings of EMNLP, 158-65, Barcelona, Spain: Association for Computational Linguistics.
2. Andrews, M., G. Vigliocco, and D. Vinson 2009. Integrating experiential and distributional data to learn semantic representations. Psychological Review 116(3). 463-98.
3. Baroni, M., and A. Lenci 2010. Distributional memory: a general framework for corpus-based semantics. Computational Linguistics 36(4). 673-721.
4. Baroni, M., B. Murphy, E. Barbu, and M. Poesio 2010. Strudel: a corpus-based semantic model based on properties and types. Cognitive Science 34(2). 222-54.
5. Baroni, M., and R. Zamparelli 2010. Nouns and vectors, adjectives are matrices: representing adjective-noun constructions in semantic space. Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, 1183-93. Cambridge, MA: Association for Computational Linguistics, October.
6. Barsalou, L. W. 2008. Grounded cognition. Annual Review of Psychology 59(1). 617-45.
7. Blei, D. M., A. Y. Ng, and M. I. Jordan 2003. Latent dirichlet allocation. Journal of Machine Learning Research 3. 993-1022.
8. Bos, J. A. 2011. Survey of computational semantics: representation inference and knowledge in wide-coverage text understanding. Language and Linguistics Compass 5(6). 336-66.
9. Bruni, E., G. B. Tran, and M. Baroni 2011. Distributional semantics from text and images. Proceedings of the GEMS 2011 Workshop on GEometrical Models of Natural Language Semantics, 22-32. Edinburgh, UK: Association for Computational Linguistics, July.
10. Budanitsky, A., and G. Hirst. 2006. Evaluating Wordnet-based measures of semantic distance. Computational Linguistics 32. 13-47.
11. Burgess, C., and K. Lund 1997. Modelling parsing constraints with high-dimensional context space. Language and Cognitive Processes 12. 177-210.
12. Churchland, P. M. 1998. Conceptual similarity across sensory and neural diversity: the Fodor/lepore challenge answered. Journal of Philosophy 95. 5-32.
13. Clarke, D. 2007. A context-theoretic framework for compositionality in distributional semantics. Computational Linguistics 38(1). 41-71.
14. Clark, S., and S. Pulman 2007. Combining symbolic and distributional models of meaning. Proceedings of the AAAI Spring Symposium on Quantum Interaction, 52-5. Stanford, CA: Association for the Advancement of Artificial Intelligence.
15. Clarke, D. 2007. Context-theoretic semantics for natural language: an algebraic framework. Sussex: University of Sussex.
16. Coecke, B., M. Sadrzadeh, and S. Clark 2010. Mathematical foundations for a compositional distributed model of meaning. Lambek Festschrift Linguistic Analysis, 36. 345-84.
17. Deerwester, S., S. T. Dumais, T. K. Landauer, G. W. Furnas, and R. A. Harshman 1990. Indexing by Latent semantic analysis. Journal of the Society for Information Science 41(6). 391-407.
18. Deschacht, K. and M. -F. Moens 2009. Semi-supervised semantic role labeling using the Latent words language model. Proceedings of EMNLP, 21-9, Singapore: Association for Computational Linguistics.
19. Devereux, B., N. Pilkington, T. Poibeau, and A. Korhonen 2009. Towards unrestricted large-scale acquisition of feature-based conceptual representations from corpus data. Research on Language and Computation 7. 137-70.
20. Dinu, G., and M. Lapata 2010. Measuring distributional similarity in context. Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, 1162-72. Cambridge, MA: Association for Computational Linguistics, October.
21. Doumas, L. A. A., and J. E. Hummel 2005. Modeling human mental representations: what works what doesnt, and why. The Cambridge handbook of thinking and reasoning, ed. by K. J. Holyoak and R. G. Morrison, 73-91. Cambridge, UK: Cambridge University Press.
22. Erk, K. 2009. Representing words as regions in vector space. Proceedings of CoNLL, Boulder, CO: Association for Computational Linguistics.
23. Erk, K. 2010. What is word meaning, really? (and how can distributional models help us describe it?). Proceedings of the 2010 Workshop on GEometrical Models of Natural Language Semantics, 17-26. Uppsala, Sweden: Association for Computational Linguistics.
24. Erk, K., and S. Padó 2008. A structured vector space model for word meaning in context. Proceedings of EMNLP, Honolulu, HI: Association for Computational Linguistics.
25. Erk, K., and S. Padó 2010. Exemplar-based models for word meaning in context. Proceedings of ACL, Uppsala, Sweden: Association for Computational Linguistics.
26. Feng, Y., and M. Lapata 2010. Visual information in semantic representation. Human language technologies: the 2010 Annual Conference of the North American chapter of the Association for Computational Linguistics, 91-9. Los Angeles, California: Association for Computational Linguistics.
27. Firth, J. R. 1957. A synopsis of linguistic theory 1930-1955. Studies in linguistic analysis, 1-32. Oxford: England: Blackwell Publishers.
28. Fodor, J. 1997. Connectionism and the problem of systematicity: why smolensky's solution still doesn't work. Cognition 62. 109-19.
29. Fodor, J., and E. Lepore 1992. Holism: a shopper's guide. Cambridge: Blackwell.
30. Fodor, J., and E. Lepore 1999. All at sea in semantic space: Churchland on meaning similarity. Journal of Philosophy 96. 381-403.
31. Gärdenfors, P. 2004. Conceptual spaces. Cambridge, MA: MIT press.
32. Garrette, D., K. Erk, and R. Mooney 2011. Integrating logical representations with probabilistic information using markov logic. Proceedings of the Ninth International Conference on Computational Semantics (IWCS 2011), 105-14. Oxford, UK: Special Interest Group on Computational Semantics of the Association for Computational Linguistics.
33. Gorman, J., and J. R. Curran 2006. Scaling distributional similarity to large corpora. ACL '06: Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the ACL, Sydney.
34. Grefenstette, E. and M. Sadrzadeh 2011. Experimental support for a categorical compositional distributional model of meaning. Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, 1394-404. Edinburgh, Scotland, UK: Association for Computational Linguistics, July.
35. Guevara, E. 2010. A regression model of adjective-noun compositionality in distributional semantics. Proceedings of the 2010 Workshop on GEometrical Models of Natural Language Semantics, 33-7. Uppsala, Sweden: Association for Computational Linguistics July.
36. Guevara, E. 2011. Computing semantic compositionality in distributional semantics. Proceedings of IWCS-9, Oxford, UK: Special Interest Group on Computational Semantics of the Association for Computational Linguistics.
37. Hanks, P. 2000. Do word meanings exist? Computers and the Humanities 34. 205-15.
38. Harris, Z. 1954. Distributional structure. Word 10. 146-62.
39. Hartung, M., and A. Frank 2011. Assessing interpretable, attribute-related meaning representations for adjective-noun phrases in a similarity prediction task. Proceedings of the GEMS 2011 Workshop on GEometrical Models of Natural Language Semantics, 52-61. Edinburgh, UK: Association for Computational Linguistics, July.
40. Hindle, D. 1990. Noun classification from predicate-argument structures. Proceedings of the 27th Annual Meeting of the Association for Computational Linguistics (ACL'90), Pittsburg, Pennsylvania: Association for Computational Linguistics.
41. Hofmann, T. 1999. Probabilistic latent semantic indexing. Proceedings of the Twenty-Second Annual International SIGIR Conference on Research and Development in Information Retrieval, University of Berkeley, CA.
42. Horgan, T., and J. Tienson 1989. Representations without rules. Philosophical Topics 17. 147-74.
43. Horgan, T., and J. Tienson 1990. Soft laws. Midwest studies in Philosophy 15. 256-79.
44. Jones, M. N., and D. J. K. Mewhort 2007. Representing word meaning and order information in a composite holographic lexicon. Psychological Review 114. 1-37.
45. Kilgarriff, A. 1997. I don't believe in word senses. Computers and the Humanities 31. 91-113.
46. Kintsch, W. 2001. Predication. Cognitive Science 25. 173-202.
47. Kintsch, W. 2007. Meaning in context. Handbook of Latent semantic analysis, ed. by T. K. Landauer, D. McNamara, S. Dennis, and W. Kintsch, 89-105. Mahwah, NJ: Erlbaum.
48. Kremer, G., and M. Baroni 2010. Predicting cognitively salient modifiers of the constitutive parts of concepts. Proceedings of the 2010 Workshop on Cognitive Modeling and Computational Linguistics, 54-62. Uppsala, Sweden: Association for Computational Linguistics, July.
49. Landauer, T., and S. Dumais 1997. A solution to Plato's problem: the latent semantic analysis theory of acquisition, induction, and representation of knowledge. Psychological Review 104(2). 211-40.
50. Leong, C. W., and R. Mihalcea 2011. Measuring the semantic relatedness between words and images. Proceedings of IWCS-9, Oxford, UK: Special Interest Group on Computational Semantics of the Association for Computational Linguistics.
51. Lin, D. 1998. Automatic retrieval and clustering of similar words. COLING-ACL98, Montreal, Canada: Association for Computational Linguistics.
52. Lin, D., and P. Pantel 2001. Discovery of inference rules for question answering. Natural Language Engineering 7(4). 343-60.
53. Louwerse, M. M. 2007. Symbolic or embodied representations: a case for symbol interdependency. Handbook of Latent semantic analysis, ed. by T. Landauer, D. McNamara S. Dennis and W. Kintsch, 107-20. Mahwah, NJ: Erlbaum.
54. Louwerse, M. M. 2010. Symbol interdependency in symbolic and embodied cognition. Topics in Cognitive Science 3. 273-302.
55. Lowe, W. 2001. Towards a theory of semantic space. Proceedings of the Cognitive Science Society, 576-81. Mahwah, NJ: Erlbaum.
56. Lund, K., C. Burgess, and R. A. Atchley 1995. Semantic and associative priming in high-dimensional semantic space. Proceedings of the Seventeenth Annual Meeting of the Cognitive Science Science Society, 660-5. Cognitive Science Society, University of Pittsburgh.
57. Manning, C. D., P. Raghavan, and H. Schütze 2008. Introduction to information retrieval. Cambridge: Cambridge University Press.
58. McCarthy, D., R. Koeling, J. Weeds, and J. Carroll 2004. Finding predominant senses in untagged text. Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL'04), 280-7. Barcelona, Spain: Association for Computational Linguistics.
59. McCarthy, D., and R. Navigli 2009. The English Lexical substitution task. Language Resources and Evaluation 43(2). 139-59. Special Issue on Computational Semantic Analysis of Language: SemEval-2007 and Beyond
60. McDonald, S., and M. Ramscar 2001. Testing the distributional hypothesis: the influence of context on judgements of semantic similarity. Proceedings of the Cognitive Science Society, 611-6. Cognitive Science Society, Edinburgh, Scotland.
61. McRae, K., G. S. Cree, M. S. Seidenberg, and C. McNorgan 2005. Semantic feature production norms for a large set of living and nonliving things. Behavior Research Methods 37(4). 547-59.
62. McRae, K., M. Spivey-Knowlton, and M. Tanenhaus 1998. Modeling the influence of thematic fit (and other constraints) in on-line sentence comprehension. Journal of Memory and Language 38. 283-312.
63. Mitchell, J., and M. Lapata 2008. Vector-based models of semantic composition. Proceedings of ACL, Columbus, OH: Association for Computational Linguistics.
64. Mitchell, J., and M. Lapata 2010. Composition in distributional models of semantics. Cognitive Science 34(8): 1388-429.
65. Montague, R. 1970. English as a formal language. Linguaggi nella societá e nella tecnica, ed. by B. Visentini, 189-224. Milan: Edizioni di Comunitá.
66. Moon, T., and K. Erk forthcoming. An inference-based model of word meaning in context as a paraphrase distribution. ACM Transactions on Intelligent Systems and Technology special issue on paraphrasing to appear.
67. Padó, S., and M. Lapata 2007. Dependency-based construction of semantic space models. Computational Linguistics 33(2). 161-99.
68. Padó, S., U. Padó, and K. Erk 2007. Flexible, corpus-based modelling of human plausibility judgements. Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL), 400-9. Prague, Czech Republic: Association for Computational Linguistics, June.
69. Preller, A., and V. Prince 2010. A quantum logic for compositional functional semantics and distributional semantic models. ESSLLI workshop on compositionality and distributional semantic models. Copenhagen: Association for Logic, Language and Information.
70. Preller, A., and M. Sadrzadeh 2011. Bell states and negative sentences in the distributed model of meaning. Electronic Notes in Theoretical Computer Science 270(2). 141-53.
71. Ravichandran, D., P. Pantel, and E. Hovy 2005. Randomized algorithms and NLP: using locality sensitive hash functions for high speed noun clustering. Proceedings of Annual Meeting of the Association for Computational Linguistics (ACL-05), 622-9. Ann Arbor, MI: Association for Computational Linguistics.
72. Reddy, S., I. P. Klapaftis, D. McCarthy, and S. Manandhar 2011. Dynamic and static prototype vectors for semantic composition. Proceedings of The 5th International Joint Conference on Natural Language Processing 2011 (IJCNLP 2011), Chiang Mai, Thailand: Association for Computational Linguistics, November 2011.
73. Reisinger, J., and R. J. Mooney 2010. Multi-prototype vector-space models of word meaning. Proceeding of NAACL. Los Angeles, CA: Association for Computational Linguistics.
74. Rubenstein, H., and J. Goodenough 1965. Contextual correlates of synonymy. Computational Linguistics 8. 627-33.
75. Schütze, H. 1998. Automatic word sense discrimination. Computational Linguistics 24(1). 97-123.
76. Smolensky, P. 1990. Tensor product variable binding and the representation of symbolic structures in connectionist systems. Artificial Intelligence, 46. 159-216.
77. Snow, R., D. Jurafsky, and Y. N. Andrew 2006. Semantic taxonomy induction from heterogenous evidence. Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, 801-8. Sydney, Australia: Association for Computational Linguistics.
78. Socher, R., E. H. Huang, J. Pennin, A. Y. Ng, and C. D. Manning 2011. Dynamic pooling and unfolding recursive autoencoders for paraphrase detection. Advances in Neural Information Processing Systems, ed. by J. Shawe-Taylor, R. S. Zemel, P. Bartlett, F. C. N. Pereira and K. Q. Weinberger, 24, 801-9. Granada: Neural Information Processing Systems Foundation.
79. Thater, S., G. Dinu, and M. Pinkal 2009. Ranking paraphrases in context. Proceedings of the ACL Workshop on Applied Textual Inference, Singapore: Association for Computational Linguistics.
80. Thater, S., H. Fürstenau, and M. Pinkal 2010. Contextualizing semantic representations using syntactically enriched vector models. Proceedings of ACL, Uppsala, Sweden: Association for Computational Linguistics.
81. Thater, S., H. Fürstenau, and M. Pinkal 2011. Word meaning in context: a simple and effective vector model. Proceedings of the 5th International Joint Conference on Natural Language Processing (IJCNLP), Chiang Mai, Thailand: Association for Computational Linguistics.
82. Turney, P. D. 2006. Similarity of semantic relations. Computational Linguistics 32(3). 379-416.
83. Turney, P. D., and P. Pantel 2010. From frequency to meaning: vector space models of semantics. Journal of Artificial Intelligence Research 37. 141-88.
84. Van de Cruys T., T. Poibeau, and A. Korhonen 2011. Latent vector weighting for word meaning in context. Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, 1012-22. Scotland, UK, Edinburgh: Association for Computational Linguistics, July.
85. Vigliocco, G., D. P. Vinson, W. Lewis, and M. F. Garrett 2004. Representing the meanings of object and action words: the featural and unitary semantic space hypothesis. Cognitive Psychology 48. 422-88.
86. Washtell, J. 2010. Expectation vectors: a semiotics inspired approach to geometric lexical-semantic representation. Proceedings of the 2010 Workshop on GEometrical Models of Natural Language Semantics, 45-50. Uppsala, Sweden: Association for Computational Linguistics, July.
87. Washtell, J. 2011. Compositional expectation: a purely distributional model of compositional semantics. Proceedings of IWCS-9, Oxford, UK: Special Interest Group on Computational Semantics of the Association for Computational Linguistics.
88. Wittgenstein, L. 1997. Philosophical investigations. Oxford, England: Blackwell Publishers. Original work published 1953.
89. Wu, S., and W. Schuler 2011. Structured composition of semantic vectors. Proceedings of IWCS-9, Oxford, UK: Special Interest Group on Computational Semantics of the Association for Computational Linguistics.
90. Zanzotto, F. M., I. Korkontzelos, F. Fallucchi, and S. Manandhar 2010. Estimating linear models for compositional distributional semantics. Proceedings of the 23rd International Conference on Computational Linguistics (COLING). Beijing: Association for Computational Linguistics.