Metaphor Identification in Large Texts Corpora
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Neuman-2013-Metaphor Identificat.pdf
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Author(s)
Neuman, Yair
Assaf, Dan
Cohen, Yohai
Last, Mark
Argamon, Shlomo
Howard, Newton
Frieder, Ophir
Date Issued
April 2013
Journal
PLoS ONE
Publisher
Public Library of Science
Citation
Neuman, Yair, Dan Assaf, Yohai Cohen, Mark Last, Shlomo Argamon, Newton Howard, and Ophir Frieder. “Metaphor Identification in Large Texts Corpora.” Edited by Eduardo G. Altmann. PLoS ONE 8, no. 4 (April 29, 2013): e62343.
Version
Final published version
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Media Laboratory
Massachusetts Institute of Technology. Synthetic Intelligence Laboratory
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Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.1371/journal.pone.0062343
