Efficient non-parametric estimation of multiple embeddings perword in vector space

EMNLP 2014 - 2014 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference

Volumn , Issue , 2014, Pages 1059-1069

  Neelakantan, Arvind ^a   Shankar, Jeevan ^a   Passos, Alexandre ^a   McCallum, Andrew ^a  

^a Biologically Inspired Neural and Dynamical Systems Laboratory   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EMBEDDINGS; NATURAL LANGUAGE PROCESSING SYSTEMS; SCALABILITY;

FAST ESTIMATION; IN CONTEXTS; ITS EFFICIENCIES; NON-PARAMETRIC ESTIMATIONS; RELATED WORKS; SINGLE VECTORS; STATE OF THE ART; WORD SIMILARITY;

VECTOR SPACES;

EID: 84961311338     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.3115/v1/d14-1113     Document Type: Conference Paper

References (29)

1. Mohit Bansal, Kevin Gimpel, and Karen Livescu. 2014. Tailoring Continuous Word Representations for Dependency Parsing. Association for Computational Linguistics (ACL).
2. Yoshua Bengio, Rejean Ducharme, Pascal Vincent, and Christian Jauvin. 2003. A neural probabilistic language model. Journal of Machine Learning Research (JMLR).
3. Peter F. Brown, Peter V. Desouza, Robert L. Mercer, Vincent J. Della Pietra, and Jenifer C. Lai. 1992. Class-based N-gram models of natural language Computational Linguistics.
4. Ronan Collobert and Jason Weston. 2008. A Unified Architecture for Natural Language Processing: Deep Neural Networks with Multitask Learning. International Conference on Machine learning (ICML).
5. Paramveer S. Dhillon, Dean Foster, and Lyle Ungar. 2011. Multi-View Learning of Word Embeddings via CCA. Advances in Neural Information Processing Systems (NIPS).
6. John Duchi, Elad Hazan, and Yoram Singer 2011. Adaptive sub- gradient methods for online learning and stochastic optimization. Journal of Machine Learning Research (JMLR).
7. Lev Finkelstein, Evgeniy Gabrilovich, Yossi Matias, Ehud Rivlin, Zach Solan, Gadi Wolfman, and Eytan Ruppin. 2001. Placing search in context: The concept revisited. International Conference on World Wide Web (WWW).
8. Evgeniy Gabrilovich and Shaul Markovitch. 2007. Computing semantic relatedness using wikipediabased explicit semantic analysis. International Joint Conference on Artificial Intelligence (IJCAI).
9. Eric H. Huang, Richard Socher, Christopher D. Manning, and Andrew Y. Ng. 2012. Improving Word Representations via Global Context and Multiple Word Prototypes. Association of Computational Linguistics (ACL).
10. Terry Koo, Xavier Carreras, and Michael Collins. 2008. Simple Semi-supervised Dependency Parsing. Association for Computational Linguistics (ACL).
11. Quoc V. Le and Tomas Mikolov. 2014 Distributed Representations of Sentences and Documents. International Conference on Machine Learning (ICML).
12. Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts. 2011 Learning Word Vectors for Sentiment Analysis Association for Computational Linguistics (ACL).
13. Adam Meyerson. 2001 IEEE Symposium on Foundations of Computer Science. International Conference on Machine Learning (ICML).
14. Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013a. Efficient Estimation of Word Representations in Vector Space. Workshop at International Conference on Learning Representations (ICLR).
15. Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013b. Distributed Representations of Words and Phrases and their Compositionality. Advances in Neural Information Processing Systems (NIPS).
16. Tomas Mikolov, Quoc V. Le, and Ilya Sutskever. 2013c. Exploiting Similarities among Languages for Machine Translation. arXiv.
17. Scott Miller, Jethran Guinness, and Alex Zamanian. 2004. Name tagging with word clusters and discriminative training. North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT).
18. Andriy Mnih and Geoffrey Hinton. 2007. Three new graphical models for statistical language modelling. International Conference on Machine learning (ICML).
19. Arvind Neelakantan and Michael Collins. 2014. Learning Dictionaries for Named Entity Recognition using Minimal Supervision. European Chapter of the Association for Computational Linguistics (EACL).
20. Alexandre Passos, Vineet Kumar, and Andrew McCallum. 2014. Lexicon Infused Phrase Embeddings for Named Entity Resolution. Conference on Natural Language Learning (CoNLL).
21. Lev Ratinov and Dan Roth. 2009. Design Challenges and Misconceptions in Named Entity Recognition. Conference on Natural Language Learning (CoNLL).
22. Siva Reddy, Ioannis P. Klapaftis, and Diana McCarthy. 2011. Dynamic and Static Prototype Vectors for Semantic Composition. International Joint Conference on Artificial Intelligence (IJCNLP).
23. Joseph Reisinger and Raymond J. Mooney. 2010a. Multi-prototype vector-space models of word meaning. North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT).
24. Joseph Reisinger and Raymond Mooney. 2010b. A mixture model with sharing for lexical semantics. Empirical Methods in Natural Language Processing (EMNLP).
25. Cyrus Shaoul and Chris Westbury. 2010. The Westbury lab wikipedia corpus.
26. Richard Socher, Eric H. Huang, Jeffrey Pennington, Andrew Y. Ng, and Christopher D. Manning. 2011 Dynamic Pooling and Unfolding Recursive Autoencoders for Paraphrase Detection. Advances in Neural Information Processing Systems (NIPS).
27. Oscar Tackstrom, Ryan McDonald, and Jakob Uszkoreit. 2012. Cross-lingual Word Clusters for Direct Transfer of Linguistic Structure. North American Chapter of the Association for Computational Linguistics: Human Language Technologies.
28. Joseph Turian, Lev Ratinov, and Yoshua Bengio. 2010. Word Representations: A Simple and General Method for Semi-Supervised Learning. Association for Computational Linguistics (ACL).
29. Will Y. Zou, Richard Socher, Daniel Cer, and Christopher D. Manning. 2013. Bilingual Word Embeddings for Phrase-Based Machine Translation. Empirical Methods in Natural Language Processing.