Word Embedding and WordNet Based Metaphor Identification and Interpretation

Rui Mao; Chenghua Lin; Frank Guerin

Word Embedding and WordNet Based Metaphor Identification and Interpretation

Rui Mao, Chenghua Lin, Frank Guerin

Research output: Chapter in Book/Report/Conference proceeding › Published conference contribution

95 Citations (Scopus)

67 Downloads (Pure)

Abstract

Metaphoric expressions are widespread in natural language, posing a significant challenge for various natural language processing tasks such as Machine Translation. Current word embedding based metaphor identification models cannot identify the exact metaphorical words within a sentence. In this paper, we propose an unsupervised learning method that identifies and interprets metaphors at word-level without any preprocessing, outperforming strong baselines in the metaphor identification task. Our model extends to interpret the identified metaphors, paraphrasing them into their literal counterparts, so that they can be better translated by machines. We evaluated this with two popular translation systems for English to Chinese, showing that our model improved the systems significantly.

Original language	English
Title of host publication	Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics
Place of Publication	Melbourne, Australia
Publisher	Association for Computational Linguistics (ACL)
Pages	1222-1231
Number of pages	10
Volume	1: Long Papers
ISBN (Print)	978-1-948087-32-2
Publication status	Published - 17 Jul 2018
Event	The 56th Annual Meeting of the Association for Computational Linguistics - Melbourne, Australia Duration: 15 Jul 2018 → 20 Jul 2018

Conference

Conference	The 56th Annual Meeting of the Association for Computational Linguistics
Country/Territory	Australia
City	Melbourne
Period	15/07/18 → 20/07/18

Keywords

Metaphor Identification
Metaphor Interpretation
Word Embedding output vector

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Word Embedding and WordNet Based Metaphor Identification and Interpretation
Licensed on a Creative Commons Attribution 4.0 License. https://creativecommons.org/licenses/by/4.0/
Final published version, 601 KBLicence: CC BY

http://aclweb.org/anthology/P18-1113Licence: CC BY

Cite this

Word Embedding and WordNet Based Metaphor Identification and Interpretation. / Mao, Rui; Lin, Chenghua; Guerin, Frank.
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics . Vol. 1: Long Papers Melbourne, Australia: Association for Computational Linguistics (ACL), 2018. p. 1222-1231.

Research output: Chapter in Book/Report/Conference proceeding › Published conference contribution

Mao, R, Lin, C & Guerin, F 2018, Word Embedding and WordNet Based Metaphor Identification and Interpretation. in Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics . vol. 1: Long Papers, Association for Computational Linguistics (ACL), Melbourne, Australia, pp. 1222-1231, The 56th Annual Meeting of the Association for Computational Linguistics, Melbourne, Australia, 15/07/18. <http://aclweb.org/anthology/P18-1113>

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abstract = "Metaphoric expressions are widespread in natural language, posing a significant challenge for various natural language processing tasks such as Machine Translation. Current word embedding based metaphor identification models cannot identify the exact metaphorical words within a sentence. In this paper, we propose an unsupervised learning method that identifies and interprets metaphors at word-level without any preprocessing, outperforming strong baselines in the metaphor identification task. Our model extends to interpret the identified metaphors, paraphrasing them into their literal counterparts, so that they can be better translated by machines. We evaluated this with two popular translation systems for English to Chinese, showing that our model improved the systems significantly.",

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isbn = "978-1-948087-32-2",

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booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics",

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AU - Guerin, Frank

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N2 - Metaphoric expressions are widespread in natural language, posing a significant challenge for various natural language processing tasks such as Machine Translation. Current word embedding based metaphor identification models cannot identify the exact metaphorical words within a sentence. In this paper, we propose an unsupervised learning method that identifies and interprets metaphors at word-level without any preprocessing, outperforming strong baselines in the metaphor identification task. Our model extends to interpret the identified metaphors, paraphrasing them into their literal counterparts, so that they can be better translated by machines. We evaluated this with two popular translation systems for English to Chinese, showing that our model improved the systems significantly.

AB - Metaphoric expressions are widespread in natural language, posing a significant challenge for various natural language processing tasks such as Machine Translation. Current word embedding based metaphor identification models cannot identify the exact metaphorical words within a sentence. In this paper, we propose an unsupervised learning method that identifies and interprets metaphors at word-level without any preprocessing, outperforming strong baselines in the metaphor identification task. Our model extends to interpret the identified metaphors, paraphrasing them into their literal counterparts, so that they can be better translated by machines. We evaluated this with two popular translation systems for English to Chinese, showing that our model improved the systems significantly.

KW - Metaphor Identification

KW - Metaphor Interpretation

KW - Word Embedding output vector

M3 - Published conference contribution

SN - 978-1-948087-32-2

VL - 1: Long Papers

SP - 1222

EP - 1231

BT - Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics

PB - Association for Computational Linguistics (ACL)

CY - Melbourne, Australia

T2 - The 56th Annual Meeting of the Association for Computational Linguistics

Y2 - 15 July 2018 through 20 July 2018

ER -

Word Embedding and WordNet Based Metaphor Identification and Interpretation

Abstract

Conference

Keywords

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