Metaphor Identification in Large Texts Corpora
Identifying metaphorical language-use (e.g., sweet child) is one of the challenges facing natural language processing. This paper describes three novel algorithms for automatic metaphor identification. The algorithms are variations of the same core algorithm. We evaluate the algorithms on two corpor...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3639214/ https://www.ncbi.nlm.nih.gov/pubmed/23658625 http://dx.doi.org/10.1371/journal.pone.0062343 |
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author | Neuman, Yair Assaf, Dan Cohen, Yohai Last, Mark Argamon, Shlomo Howard, Newton Frieder, Ophir |
author_facet | Neuman, Yair Assaf, Dan Cohen, Yohai Last, Mark Argamon, Shlomo Howard, Newton Frieder, Ophir |
author_sort | Neuman, Yair |
collection | PubMed |
description | Identifying metaphorical language-use (e.g., sweet child) is one of the challenges facing natural language processing. This paper describes three novel algorithms for automatic metaphor identification. The algorithms are variations of the same core algorithm. We evaluate the algorithms on two corpora of Reuters and the New York Times articles. The paper presents the most comprehensive study of metaphor identification in terms of scope of metaphorical phrases and annotated corpora size. Algorithms’ performance in identifying linguistic phrases as metaphorical or literal has been compared to human judgment. Overall, the algorithms outperform the state-of-the-art algorithm with 71% precision and 27% averaged improvement in prediction over the base-rate of metaphors in the corpus. |
format | Online Article Text |
id | pubmed-3639214 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-36392142013-05-08 Metaphor Identification in Large Texts Corpora Neuman, Yair Assaf, Dan Cohen, Yohai Last, Mark Argamon, Shlomo Howard, Newton Frieder, Ophir PLoS One Research Article Identifying metaphorical language-use (e.g., sweet child) is one of the challenges facing natural language processing. This paper describes three novel algorithms for automatic metaphor identification. The algorithms are variations of the same core algorithm. We evaluate the algorithms on two corpora of Reuters and the New York Times articles. The paper presents the most comprehensive study of metaphor identification in terms of scope of metaphorical phrases and annotated corpora size. Algorithms’ performance in identifying linguistic phrases as metaphorical or literal has been compared to human judgment. Overall, the algorithms outperform the state-of-the-art algorithm with 71% precision and 27% averaged improvement in prediction over the base-rate of metaphors in the corpus. Public Library of Science 2013-04-29 /pmc/articles/PMC3639214/ /pubmed/23658625 http://dx.doi.org/10.1371/journal.pone.0062343 Text en © 2013 Neuman et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Neuman, Yair Assaf, Dan Cohen, Yohai Last, Mark Argamon, Shlomo Howard, Newton Frieder, Ophir Metaphor Identification in Large Texts Corpora |
title | Metaphor Identification in Large Texts Corpora |
title_full | Metaphor Identification in Large Texts Corpora |
title_fullStr | Metaphor Identification in Large Texts Corpora |
title_full_unstemmed | Metaphor Identification in Large Texts Corpora |
title_short | Metaphor Identification in Large Texts Corpora |
title_sort | metaphor identification in large texts corpora |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3639214/ https://www.ncbi.nlm.nih.gov/pubmed/23658625 http://dx.doi.org/10.1371/journal.pone.0062343 |
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