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Metaphor Identification in Large Texts Corpora

Author

Listed:
  • Yair Neuman
  • Dan Assaf
  • Yohai Cohen
  • Mark Last
  • Shlomo Argamon
  • Newton Howard
  • Ophir Frieder

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.

Suggested Citation

  • Yair Neuman & Dan Assaf & Yohai Cohen & Mark Last & Shlomo Argamon & Newton Howard & Ophir Frieder, 2013. "Metaphor Identification in Large Texts Corpora," PLOS ONE, Public Library of Science, vol. 8(4), pages 1-9, April.
    • Handle: RePEc:plo:pone00:0062343
    DOI: 10.1371/journal.pone.0062343
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    References listed on IDEAS

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    1. Paul H Thibodeau & Lera Boroditsky, 2011. "Metaphors We Think With: The Role of Metaphor in Reasoning," PLOS ONE, Public Library of Science, vol. 6(2), pages 1-11, February.
    2. Steven Pinker & George Lakoff, 2007. "Does language frame politics?," Public Policy Review, Institute for Public Policy Research, vol. 14(1), pages 59-71, March.
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    1. Martina Ptiček & Jasminka Dobša, 2023. "Methods of Annotating and Identifying Metaphors in the Field of Natural Language Processing," Future Internet, MDPI, vol. 15(6), pages 1-28, May.

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