Ontologically grounded multi-sense representation learning for semantic vector space models

NAACL HLT 2015 - 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference

Volumn , Issue , 2015, Pages 683-693

  Jauhar, Sujay Kumar ^a   Dyer, Chris ^a   Hovy, Eduard ^a  

^a Carnegie Mellon University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL LINGUISTICS; EMBEDDINGS; MAXIMUM LIKELIHOOD; ONTOLOGY; PREDICTIVE ANALYTICS; SEMANTICS; VECTORS;

DISTRIBUTIONAL INFORMATION; LATENT VARIABLE; LEXICAL ONTOLOGY; LEXICAL SEMANTICS; SEMANTIC VECTORS; SINGLE VECTORS; VECTOR SPACE MODELS; WORD SENSE;

VECTOR SPACES;

EID: 84960146172     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.3115/v1/n15-1070     Document Type: Conference Paper

References (44)

1. Elia Bruni, Nam-Khanh Tran, and Marco Baroni. 2014. Multimodal distributional semantics. Journal of Artificial Intelligence Research (JAIR), 49:1-47.
2. Bob Carpenter. 2008. Lazy sparse stochastic gradient descent for regularized multinomial logistic regression. technical report, alias-i. available at http://lingpipeblog.com/lingpipe-white-papers.
3. Ronan Collobert and Jason Weston. 2008. A unified architecture for natural language processing: deep neural networks with multitask learning. In Proceedings of the 25th international conference on Machine learning, ICML '08, pages 160-167, New York, NY, USA. ACM.
4. Adrian Corduneanu and Tommi Jaakkola. 2002. On information regularization. In Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence, pages 151-158. Morgan Kaufmann Publishers Inc.
5. Dipanjan Das and Slav Petrov. 2011. Unsupervised partof-speech tagging with bilingual graph-based projections. In Proc. of ACL.
6. Dipanjan Das and Noah A. Smith. 2011. Semisupervised frame-semantic parsing for unknown predicates. In Proc. of ACL.
7. S. C. Deerwester, S. T. Dumais, T. K. Landauer, G. W. Furnas, and R. A. Harshman. 1990. Indexing by latent semantic analysis. Journal of the American Society for Information Science.
8. Georgiana Dinu and Mirella Lapata. 2010. Measuring distributional similarity in context. In Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, pages 1162-1172. Association for Computational Linguistics.
9. Katrin Erk and Sebastian Padó. 2008. A structured vector space model for word meaning in context. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, EMNLP '08, pages 897-906, Stroudsburg, PA, USA. Association for Computational Linguistics.
10. Katrin Erk and Sebastian Padó. 2010. Exemplar-based models for word meaning in context. In Proceedings of the ACL 2010 Conference Short Papers, pages 92-97. Association for Computational Linguistics.
11. Manaal Faruqui, Jesse Dodge, Sujay K Jauhar, Chris Dyer, Eduard Hovy, and Noah A Smith. 2014. Retrofitting word vectors to semantic lexicons. arXiv preprint arXiv:1411.4166.
12. Lev Finkelstein, Evgeniy Gabrilovich, Yossi Matias, Ehud Rivlin, Zach Solan, Gadi Wolfman, and Eytan Ruppin. 2002. Placing search in context: The concept revisited. In ACM Transactions on Information Systems, volume 20, pages 116-131, January.
13. Juri Ganitkevitch, Benjamin Van Durme, and Chris Callison-Burch. 2013. PPDB: The paraphrase database. In Proceedings of NAACL-HLT, pages 758-764, Atlanta, Georgia, June. Association for Computational Linguistics.
14. Jiang Guo, Wanxiang Che, Haifeng Wang, and Ting Liu. 2014. Learning sense-specific word embeddings by exploiting bilingual resources. In Proceedings of COLING, pages 497-507.
15. Eric H Huang, Richard Socher, Christopher D Manning, and Andrew Y Ng. 2012. Improving word representations via global context and multiple word prototypes. In Proceedings of the 50th ACL: Long Papers-Volume 1, pages 873-882.
16. Mario Jarmasz and Stan Szpakowicz. 2004. Roget's thesaurus and semantic similarity. Recent Advances in Natural Language Processing III: Selected Papers from RANLP, 2003:111.
17. Mark Johnson. 2007. Why doesn't em find good hmm pos-taggers? In EMNLP-CoNLL, pages 296-305. Citeseer.
18. Adam Kilgarriff. 1997. I don't believe in word senses. Computers and the Humanities, 31(2):91-113.
19. Thomas K Landauer and Susan T. Dumais. 1997. A solution to plato's problem: The latent semantic analysis theory of acquisition, induction, and representation of knowledge. Psychological review, pages 211-240.
20. Christopher D. Manning, Prabhakar Raghavan, and Hinrich Schütze. 2008. Introduction to Information Retrieval. Cambridge University Press, New York, NY, USA.
21. Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013a. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781.
22. Tomas Mikolov, Wen-tau Yih, and Geoffrey Zweig. 2013b. Linguistic regularities in continuous space word representations. In Proceedings of the 2013 Conference of the NAACL: Human Language Technologies, pages 746-751, Atlanta, Georgia, June.
23. George Miller andWalter Charles. 1991. Contextual correlates of semantic similarity. In Language and Cognitive Processes, pages 1-28.
24. George A Miller. 1995. Wordnet: a lexical database for english. Communications of the ACM, 38(11):39-41.
25. Jeff Mitchell and Mirella Lapata. 2008. Vector-based models of semantic composition. In Proceedings of ACL-08: HLT, pages 236-244.
26. Andriy Mnih and Yee Whye Teh. 2012. A fast and simple algorithm for training neural probabilistic language models. In Proceedings of the 29th International Conference on Machine Learning, pages 1751-1758.
27. Arvind Neelakantan, Jeevan Shankar, Alexandre Passos, and Andrew McCallum. 2014. Efficient nonparametric estimation of multiple embeddings per word in vector space. In Proceedings of EMNLP.
28. Ted Pedersen and Varada Kolhatkar. 2009. Wordnet:: Senserelate:: Allwords: a broad coverage word sense tagger that maximizes semantic relatedness. In Proceedings of human language technologies: The 2009 annual conference of the north american chapter of the association for computational linguistics, companion volume: Demonstration session, pages 17-20. Association for Computational Linguistics.
29. Mohammad Taher Pilehvar, David Jurgens, and Roberto Navigli. 2013. Align, disambiguate and walk: A unified approach for measuring semantic similarity. In ACL (1), pages 1341-1351.
30. Siva Reddy, Ioannis P Klapaftis, Diana McCarthy, and Suresh Manandhar. 2011. Dynamic and static prototype vectors for semantic composition. In IJCNLP, pages 705-713.
31. Joseph Reisinger and Raymond J. Mooney. 2010. Multiprototype vector-space models of word meaning. In Proceedings of the 11th Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-2010), pages 109-117.
32. Herbert Rubenstein and John B. Goodenough. 1965. Contextual correlates of synonymy. Commun. ACM, 8(10):627-633, October.
33. Diarmuid Ó Séaghdha and Anna Korhonen. 2011. Probabilistic models of similarity in syntactic context. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, EMNLP '11, pages 1047-1057, Stroudsburg, PA, USA. Association for Computational Linguistics.
34. Amarnag Subramanya and Jeff A Bilmes. 2009. Entropic graph regularization in non-parametric semisupervised classification. In NIPS, pages 1803-1811.
35. Stefanie Tellex, Boris Katz, Jimmy J. Lin, Aaron Fernandes, and Gregory Marton. 2003. Quantitative evaluation of passage retrieval algorithms for question answering. In SIGIR, pages 41-47.
36. Stefan Thater, Hagen Fürstenau, and Manfred Pinkal. 2011. Word meaning in context: A simple and effective vector model. In IJCNLP, pages 1134-1143.
37. Fei Tian, Hanjun Dai, Jiang Bian, Bin Gao, Rui Zhang, Enhong Chen, and Tie-Yan Liu. 2014. A probabilistic model for learning multi-prototype word embeddings. In Proceedings of COLING, pages 151-160.
38. Peter D. Turney. 2001. Mining the web for synonyms: Pmi-ir versus lsa on toefl. In Proceedings of the 12th European Conference on Machine Learning, EMCL '01, pages 491-502, London, UK, UK. Springer-Verlag.
39. Peter D. Turney. 2006. Similarity of semantic relations. Comput. Linguist., 32(3):379-416, September.
40. Peter D Turney. 2013. Distributional semantics beyond words: Supervised learning of analogy and paraphrase. Transactions of the Association for Computational Linguistics, 1:353-366.
41. Tim Van de Cruys, Thierry Poibeau, and Anna Korhonen. 2011. Latent vector weighting for word meaning in context. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, pages 1012-1022. Association for Computational Linguistics.
42. Mo Yu and Mark Dredze. 2014. Improving lexical embeddings with semantic knowledge. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics.
43. Zhi Zhong and Hwee Tou Ng. 2010. It makes sense: A wide-coverage word sense disambiguation system for free text. In Proceedings of the ACL 2010 System Demonstrations, pages 78-83. Association for Computational Linguistics.
44. Xiaojin Zhu, Zoubin Ghahramani, John Lafferty, et al. 2003. Semi-supervised learning using gaussian fields and harmonic functions. In ICML, volume 3, pages 912-919.