title: Metaphor identification in large texts corpora.
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.
authors: Department of Education, Ben-Gurion University of the Negev, Beer-Sheva, Israel. yneuman@bgu.ac.il
authors: Yair
authors: Y
authors: Neuman
authors: Dan
authors: D
authors: Assaf
authors: Yohai
authors: Y
authors: Cohen
authors: Mark
authors: M
authors: Last
authors: Shlomo
authors: S
authors: Argamon
authors: Newton
authors: N
authors: Howard
authors: Ophir
authors: O
authors: Frieder
mesh_terms: D000465:Algorithms; D032882:Comprehension; D006801:Humans; D007802:Language; D019365:Metaphor; D009323:Natural Language Processing; D012660:Semantics
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