Word representations via Gaussian embedding

3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings

Volumn , Issue , 2015, Pages

  Vilnis, Luke ^a   McCallum, Andrew ^a  

^a UNIVERSITY OF MASSACHUSETTS   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BENCHMARKING; VECTOR SPACES;

COSINE SIMILARITY; DECISION BOUNDARY; DENSITY-BASED; DISTRIBUTED REPRESENTATION; GAUSSIANS; LOW-DIMENSIONAL SPACES; WORD REPRESENTATIONS;

EMBEDDINGS;

EID: 85083950597     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (38)

1. Aizerman, M. A., Braverman, E. A., and Rozonoer, L. Theoretical foundations of the potential function method in pattern recognition learning. In Automation and Remote Control,, number 25 in Automation and Remote Control,, pp. 821–837, 1964.
2. Baroni, Marco, Bernardini, Silvia, Ferraresi, Adriano, and Zanchetta, Eros. The wacky wide web: a collection of very large linguistically processed web-crawled corpora. Language resources and evaluation, 43(3):209–226, 2009.
3. Baroni, Marco, Bernardi, Raffaella, Do, Ngoc-Quynh, and Shan, Chung-chieh. Entailment above the word level in distributional semantics. In Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics, EACL’12, pp. 23–32, Stroudsburg, PA, USA, 2012. Association for Computational Linguistics.
4. Baroni, Marco, Dinu, Georgiana, and Kruszewski, Germán. Don’t count, predict! a systematic comparison of context-counting vs. context-predicting semantic vectors. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 238–247. Association for Computational Linguistics, 2014.
5. Bengio, Yoshua, Schwenk, Holger, Senécal, Jean-Sébastien, Morin, Fréderic, and Gauvain, Jean-Luc. Neural probabilistic language models. In Innovations in Machine Learning, pp. 137–186. Springer, 2006.
6. Bishop, Christopher M. Mixture density networks, 1994.
7. Brown, Gerald G and Rutemiller, Herbert C. Means and variances of stochastic vector products with applications to random linear models. Management Science, 24(2):210–216, 1977.
8. Bruni, Elia, Tran, Nam-Khanh, and Baroni, Marco. Multimodal distributional semantics. JAIR, 2014.
9. Clinchant, Stéphane and Perronnin, Florent. Textual similarity with a bag-of-embedded-words model. In Proceedings of the 2013 Conference on the Theory of Information Retrieval, ICTIR’13, pp. 25:117–25:120, New York, NY, USA, 2013a. ACM.
10. Clinchant, Stéphane and Perronnin, Florent. Aggregating continuous word embeddings for information retrieval. ACL 2013, pp. 100, 2013b.
11. Deerwester, S., Dumais, S.T., Furnas, G.W., Landauer, T.K., and Harshman, R.A. Indexing by latent semantic analysis. Journal of the American Society for Information Science 41, pp. 391–407, 1990.
12. Duchi, John, Hazan, Elad, and Singer, Yoram. Adaptive subgradient methods for online learning and stochastic optimization. The Journal of Machine Learning Research, 12:2121–2159, 2011.
13. Erk, Katrin. Representing words as regions in vector space. In Proceedings of the Thirteenth Conference on Computational Natural Language Learning, pp. 57–65. Association for Computational Linguistics, 2009.
14. Faruqui, Manaal and Dyer, Chris. Community evaluation and exchange of word vectors at wordvectors.org. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations, Baltimore, USA, June 2014. Association for Computational Linguistics.
15. Finkelstein, Lev, Gabrilovich, Evgeniy, Matias, Yossi, Rivlin, Ehud, Solan, Zach, Wolfman, Gadi, and Ruppin, Eytan. Placing search in context: The concept revisited. In Proceedings of the 10th international conference on World Wide Web, pp. 406–414. ACM, 2001.
16. Geffet, Maayan and Dagan, Ido. The distributional inclusion hypotheses and lexical entailment. In Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics, pp. 107–114. Association for Computational Linguistics, 2005.
17. Hill, Felix, Reichart, Roi, and Korhonen, Anna. Simlex-999: Evaluating semantic models with (genuine) similarity estimation. arXiv preprint arXiv:1408.3456, 2014.
18. Jebara, Tony, Kondor, Risi, and Howard, Andrew. Probability product kernels. The Journal of Machine Learning Research, 5:819–844, 2004.
19. Joachims, Thorsten. Optimizing search engines using clickthrough data. In Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 133–142. ACM, 2002.
20. Kiros, Ryan, Zemel, Richard, and Salakhutdinov, Ruslan R. A multiplicative model for learning distributed text-based attribute representations. In NIPS, 2014.
21. Kiyoshiyo, Shimaoka, Masayasu, Muraoka, Futo, Yamamoto, Watanabe, Yotaro, Okazaki, Naoaki, and Inui, Kentaro. Distribution representation of the meaning of words and phrases by a gaussian distribution. In Language Processing Society 20th Annual Conference (In Japanese), 2014.
22. Koren, Yehuda, Bell, Robert, and Volinsky, Chris. Matrix factorization techniques for recommender systems. Computer, 42(8):30–37, August 2009. ISSN 0018-9162.
23. Lanckriet, Gert RG, Cristianini, Nello, Bartlett, Peter, Ghaoui, Laurent El, and Jordan, Michael I. Learning the kernel matrix with semidefinite programming. The Journal of Machine Learning Research, 5:27–72, 2004.
24. LeCun, Yann, Chopra, Sumit, and Hadsell, Raia. A tutorial on energy-based learning. 2006.
25. Levy, Omer and Goldberg, Yoav. Neural word embedding as implicit matrix factorization. In Advances in Neural Information Processing Systems, pp. 2177–2185, 2014.
26. Mikolov, Tomas, Sutskever, Ilya, Chen, Kai, Corrado, Greg S, and Dean, Jeff. Distributed representations of words and phrases and their compositionality. In Advances in Neural Information Processing Systems, pp. 3111–3119, 2013.
27. Miller, George A and Charles, Walter G. Contextual correlates of semantic similarity. Language and cognitive processes, 6(1):1–28, 1991.
28. Mnih, Andriy and Hinton, Geoffrey E. A scalable hierarchical distributed language model. In Advances in neural information processing systems, pp. 1081–1088, 2008.
29. Mnih, Andriy and Salakhutdinov, Ruslan. Probabilistic matrix factorization. In Advances in neural information processing systems, pp. 1257–1264, 2007.
30. Petersen, Kaare Brandt. The matrix cookbook. 2006.
31. Riedel, Sebastian, Yao, Limin, McCallum, Andrew, and Marlin, Benjamin M. Relation extraction with matrix factorization and universal schemas. 2013.
32. Rubenstein, Herbert and Goodenough, John B. Contextual correlates of synonymy. Commun. ACM, 8(10):627–633, October 1965. ISSN 0001-0782.
33. Rumelhart, D.E., Hintont, G.E., and Williams, R.J. Learning representations by back-propagating errors. Nature, 323(6088):533–536, 1986.
34. Salakhutdinov, Ruslan and Mnih, Andriy. Bayesian probabilistic matrix factorization using markov chain monte carlo. In Proceedings of the 25th international conference on Machine learning, pp. 880–887. ACM, 2008.
35. Szumlanski, Sean R, Gomez, Fernando, and Sims, Valerie K. A new set of norms for semantic relatedness measures. In ACL, 2013.
36. Weston, Jason, Bengio, Samy, and Usunier, Nicolas. Wsabie: Scaling up to large vocabulary image annotation. In IJCAI, volume 11, pp. 2764–2770, 2011.
37. Xing, Eric P, Jordan, Michael I, Russell, Stuart, and Ng, Andrew Y. Distance metric learning with application to clustering with side-information. In Advances in neural information processing systems, pp. 505–512, 2002.
38. Yang, Dongqiang and Powers, David M. W. Verb similarity on the taxonomy of wordnet. In In the 3rd International WordNet Conference (GWC-06), Jeju Island, Korea, 2006.