Improving distributed representation of word sense via WordNet Gloss composition and context clustering

Tao Chen; Ruifeng Xu; Yulan He; Xuan Wang

Improving distributed representation of word sense via WordNet Gloss composition and context clustering

Tao Chen, Ruifeng Xu, Yulan He, Xuan Wang

Computer Science Research Group

Research output: Chapter in Book/Published conference output › Conference publication

Abstract

In recent years, there has been an increas-ing interest in learning a distributed rep-resentation of word sense. Traditional context clustering based models usually require careful tuning of model parame-ters, and typically perform worse on infre-quent word senses. This paper presents a novel approach which addresses these lim-itations by first initializing the word sense embeddings through learning sentence-level embeddings from WordNet glosses using a convolutional neural networks. The initialized word sense embeddings are used by a context clustering based model to generate the distributed representations of word senses. Our learned represen-tations outperform the publicly available embeddings on 2 out of 4 metrics in the word similarity task, and 6 out of 13 sub tasks in the analogical reasoning task.

Original language	English
Title of host publication	Proceedings of the 53rd annual meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Short Papers)
Publisher	Association for Computational Linguistics
Pages	15-20
Number of pages	6
Volume	2
ISBN (Print)	978-1-941643-73-0
Publication status	Published - 2015
Event	53rd annual meeting of the Association for Computational Linguistics / 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing - Beijing, China Duration: 26 Jul 2015 → 31 Jul 2015

Meeting

Meeting	53rd annual meeting of the Association for Computational Linguistics / 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing
Abbreviated title	ACL-IJCNLP 2015
Country/Territory	China
City	Beijing
Period	26/07/15 → 31/07/15

Cite this

Chen, T., Xu, R., He, Y., & Wang, X. (2015). Improving distributed representation of word sense via WordNet Gloss composition and context clustering. In Proceedings of the 53rd annual meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Short Papers) (Vol. 2, pp. 15-20). Association for Computational Linguistics.

Chen, Tao ; Xu, Ruifeng ; He, Yulan et al. / Improving distributed representation of word sense via WordNet Gloss composition and context clustering. Proceedings of the 53rd annual meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Short Papers). Vol. 2 Association for Computational Linguistics, 2015. pp. 15-20

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title = "Improving distributed representation of word sense via WordNet Gloss composition and context clustering",

abstract = "In recent years, there has been an increas-ing interest in learning a distributed rep-resentation of word sense. Traditional context clustering based models usually require careful tuning of model parame-ters, and typically perform worse on infre-quent word senses. This paper presents a novel approach which addresses these lim-itations by first initializing the word sense embeddings through learning sentence-level embeddings from WordNet glosses using a convolutional neural networks. The initialized word sense embeddings are used by a context clustering based model to generate the distributed representations of word senses. Our learned represen-tations outperform the publicly available embeddings on 2 out of 4 metrics in the word similarity task, and 6 out of 13 sub tasks in the analogical reasoning task.",

author = "Tao Chen and Ruifeng Xu and Yulan He and Xuan Wang",

year = "2015",

language = "English",

isbn = "978-1-941643-73-0",

volume = "2",

pages = "15--20",

booktitle = "Proceedings of the 53rd annual meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Short Papers)",

publisher = "Association for Computational Linguistics",

note = "53rd annual meeting of the Association for Computational Linguistics / 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing, ACL-IJCNLP 2015 ; Conference date: 26-07-2015 Through 31-07-2015",

}

Chen, T, Xu, R, He, Y & Wang, X 2015, Improving distributed representation of word sense via WordNet Gloss composition and context clustering. in Proceedings of the 53rd annual meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Short Papers). vol. 2, Association for Computational Linguistics, pp. 15-20, 53rd annual meeting of the Association for Computational Linguistics / 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing, Beijing, China, 26/07/15.

Improving distributed representation of word sense via WordNet Gloss composition and context clustering. / Chen, Tao; Xu, Ruifeng; He, Yulan et al.
Proceedings of the 53rd annual meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Short Papers). Vol. 2 Association for Computational Linguistics, 2015. p. 15-20.

Research output: Chapter in Book/Published conference output › Conference publication

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Chen T, Xu R, He Y, Wang X. Improving distributed representation of word sense via WordNet Gloss composition and context clustering. In Proceedings of the 53rd annual meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Short Papers). Vol. 2. Association for Computational Linguistics. 2015. p. 15-20

Improving distributed representation of word sense via WordNet Gloss composition and context clustering

Abstract

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