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...

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Published inPloS one Vol. 8; no. 4; p. e62343
Main Authors Neuman, Yair, Assaf, Dan, Cohen, Yohai, Last, Mark, Argamon, Shlomo, Howard, Newton, Frieder, Ophir
Format Journal Article
LanguageEnglish
Published United States Public Library of Science 29.04.2013
Public Library of Science (PLoS)
Subjects
Algorithms
Cognition & reasoning
Comprehension
Computer Science
Dictionaries
Humans
Identification
Language
Linguistics
Mathematics
Medicine
Metaphor
Metaphors
Natural language
Natural Language Processing
Neurosciences
Random variables
Semantic web
Semantics
Social and Behavioral Sciences
Theory
Word sense disambiguation
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ISSN1932-6203
1932-6203
DOI10.1371/journal.pone.0062343

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Summary: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.
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Designed the algorithms: YN DA YC ML. Conceived and designed the experiments: YN DA YC ML SA NH OF. Performed the experiments: YN DA YC. Analyzed the data: YN DA YC ML SA NH OF. Contributed reagents/materials/analysis tools: YN DA YC ML SA NH OF. Wrote the paper: YN DA YC ML SA NH OF.
Competing Interests: YC is the owner of Gilasio Coding Ltd. There are no patents, products in development or marketed products to declare. This does not alter the authors’ adherence to all the PLOS ONE policies on sharing data and materials.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0062343