Learning semanticword embeddings based on ordinal knowledge constraints

ACL-IJCNLP 2015 - 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing, Proceedings of the Conference

Volumn 1, Issue , 2015, Pages 1501-1511

  Liu, Quan ^a   Jiang, Hui ^b   Wei, Si ^c   Ling, Zhen Hua ^a   Hu, Yu ^c  

^a UNIVERSITY OF SCIENCE AND TECHNOLOGY OF CHINA   (China)

^b YORK UNIVERSITY   (Canada)

^c IFLYTEK RESEARCH   (China)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL LINGUISTICS; CONSTRAINED OPTIMIZATION; EMBEDDINGS; GRADIENT METHODS; LEARNING SYSTEMS; SEMANTICS; STOCHASTIC SYSTEMS;

CONSTRAINED OPTIMI-ZATION PROBLEMS; INEQUALITY CONSTRAINT; KNOWLEDGE CONSTRAINTS; KNOWLEDGE RESOURCE; NAME ENTITY RECOGNITION; OBJECTIVE FUNCTIONS; SENTENCE COMPLETIONS; STOCHASTIC GRADIENT DESCENT ALGORITHM;

NATURAL LANGUAGE PROCESSING SYSTEMS;

EID: 84943767019     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.3115/v1/p15-1145     Document Type: Conference Paper

References (35)

1. Yoshua Bengio, Réjean Ducharme, Pascal Vincent, and Christian Jauvin. 2003. A neural probabilistic language model. Journal of Machine Learning Research, 3:1137-1155.
2. Jiang Bian, Bin Gao, and Tie-Yan Liu. 2014. Knowledge-powered deep learning for word embedding. In Machine Learning and Knowledge Discovery in Databases, pages 132-148. Springer.
3. Zhigang Chen, Wei Lin, Qian Chen, Xiaoping Chen, Si Wei, Xiaodan Zhu, and Hui Jiang. 2015. Revisiting word embedding for contrasting meaning. In Proceedings of ACL.
4. Ronan Collobert and Jason Weston. 2008. A unified architecture for natural language processing: Deep neural networks with multitask learning. In Proceedings of ICML, pages 160-167. ACM.
5. Ronan Collobert, Jason Weston, Léon Bottou, Michael Karlen, Koray Kavukcuoglu, and Pavel Kuksa. 2011. Natural language processing (almost) from scratch. Journal of Machine Learning Research, 12:2493-2537.
6. Jacob Devlin, Rabih Zbib, Zhongqiang Huang, Thomas Lamar, Richard Schwartz, and John Makhoul. 2014. Fast and robust neural network joint models for statistical machine translation. In Proceedings of ACL, pages 1370-1380.
7. Manaal Faruqui, Jesse Dodge, Sujay K Jauhar, Chris Dyer, Eduard Hovy, and Noah A Smith. 2014. Retrofitting word vectors to semantic lexicons. In Proceedings of the NIPS Deep learning and representation learning workshop.
8. James Champlin Fernald. 1896. English synonyms and antonyms. Funk & Wagnalls Company.
9. Lev Finkelstein, Evgeniy Gabrilovich, Yossi Matias, Ehud Rivlin, Zach Solan, Gadi Wolfman, and Eytan Ruppin. 2001. Placing search in context: The concept revisited. In Proceedings of WWW, pages 406-414. ACM.
10. Ronald A Fisher. 1936. The use of multiple measurements in taxonomic problems. Annals of eugenics, 7(2):179-188.
11. Evgeniy Gabrilovich and Shaul Markovitch. 2007. Computing semantic relatedness using wikipediabased explicit semantic analysis. In Proceedings of IJCAI, volume 7, pages 1606-1611.
12. Zellig S Harris. 1954. Distributional structure. Word, 10(23):146-162.
13. Geoffrey E Hinton, James L McClelland, and David E Rumelhart. 1986. Distributed representations. In Parallel distributed processing: Explorations in the microstructure of cognition. Volume 1: Foundations, pages 77-109. MIT Press.
14. Dan Jurafsky and James H Martin. 2000. Speech & language processing. Pearson Education India.
15. Thomas K Landauer and Susan T Dumais. 1997. A solution to platos problem: The latent semantic analysis theory of acquisition, induction, and representation of knowledge. Psychological review, 104(2):211.
16. Claudia Leacock and Martin Chodorow. 1998. Combining local context and wordnet similarity for word sense identification. WordNet: An electronic lexical database, 49(2):265-283.
17. Omer Levy and Yoav Goldberg. 2014. Neural word embedding as implicit matrix factorization. In Proceedings of NIPS, pages 2177-2185.
18. David D Lewis, Yiming Yang, Tony G Rose, and Fan Li. 2004. RCV1: A new benchmark collection for text categorization research. Journal of Machine Learning Research, 5:361-397.
19. Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013a. Efficient estimation of word representations in vector space. In Proceedings of Workshop at ICLR.
20. Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013b. Distributed representations of words and phrases and their compositionality. In Proceedings of NIPS, pages 3111-3119.
21. Tomáš Mikolov. 2012. Statistical language models based on neural networks. Ph.D. Thesis, Ph. D. Thesis, Brno University of Technology.
22. George A Miller and Walter G Charles. 1991. Contextual correlates of semantic similarity. Language and cognitive processes, 6(1):1-28.
23. George A Miller. 1995. Wordnet: a lexical database for english. Communications of the ACM, 38(11):39-41.
24. Andriy Mnih and Yee Whye Teh. 2012. A fast and simple algorithm for training neural probabilistic language models. In Proceedings of ICML.
25. Jeffrey Pennington, Richard Socher, and Christopher D Manning. 2014. Glove: Global vectors for word representation. Proceedings of EMNLP, 12:1532-1543.
26. Tim Rocktäschel, Matko Bošnjak, Sameer Singh, and Sebastian Riedel. 2014. Low-dimensional embeddings of logic. In Proceedings of the ACL 2014 Workshop on Semantic Parsing, pages 45-49, Baltimore, MD, June. Association for Computational Linguistics.
27. Herbert Rubenstein and John B Goodenough. 1965. Contextual correlates of synonymy. Communications of the ACM, 8(10):627-633.
28. Holger Schwenk. 2007. Continuous space language models. Computer Speech & Language, 21(3):492-518.
29. Ilya Sutskever, Oriol Vinyals, and Quoc VV Le. 2014. Sequence to sequence learning with neural networks. In Proceedings of NIPS, pages 3104-3112.
30. Joseph Turian, Lev Ratinov, and Yoshua Bengio. 2010. Word representations: a simple and general method for semi-supervised learning. In Proceedings of ACL, pages 384-394. Association for Computational Linguistics.
31. Peter D Turney, Patrick Pantel, et al. 2010. From frequency to meaning: Vector space models of semantics. Journal of artificial intelligence research, 37(1):141-188.
32. Chang Xu, Yalong Bai, Jiang Bian, Bin Gao, Gang Wang, Xiaoguang Liu, and Tie-Yan Liu. 2014. Rcnet: A general framework for incorporating knowledge into word representations. In Proceedings of CIKM, pages 1219-1228. ACM.
33. Mo Yu and Mark Dredze. 2014. Improving lexical embeddings with semantic knowledge. In Proceedings of ACL, volume 2, pages 545-550.
34. Geoffrey Zweig and Christopher JC Burges. 2011. The microsoft research sentence completion challenge. Technical report, Technical Report MSR-TR-2011-129, Microsoft.
35. Geoffrey Zweig. 2014. Explicit representation of antonymy in language modelling. Technical report, Technical Report MSR-TR-2014-52, Microsoft.