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Statistical Metaphor Processing

Summary

Metaphor is extremely common in everyday text, yet NLP systems usually read it literally. This causes errors in machine translation, opinion mining and other semantic tasks. Earlier computational work depended on hand-built knowledge and covered narrow domains or a few phenomena. Shutova, Teufel and Korhonen present what they describe as the first unified, open-domain statistical system that both detects and interprets metaphor in unrestricted text. It focuses on lexical, mainly verb-based, metaphors and works in two stages:

  • Detection: a small seed set of metaphorical phrases is generalised using distributional clustering of verbs and nouns, where clusters act as source domains.
  • Interpretation: the system replaces each metaphorical verb with a literal paraphrase. Candidates are ranked by context, by WordNet-based similarity, and by Resnik's selectional association.

The approach needs little supervision and outputs plain text, so other applications can easily consume it. The authors report high accuracy. They also identify limitations: recall depends on the seed set, the hard clusters do not handle polysemy, and the system still relies on WordNet.

Key points

  • A cited corpus study (Shutova and Teufel 2010) finds a metaphorical expression in about one of every three sentences of general-domain text.
  • In a pilot with Google Translate (English→Russian) on the Shutova (2010) verb-metaphor sentences, 27 of 62 sentences (44%) were mistranslated because of metaphor.
  • Seed examples such as "stir excitement" (FEELINGS ARE LIQUIDS) let the system find related metaphors through verb and noun clusters. Fixed idioms are deliberately excluded.
  • Proposed extensions include:
    • hierarchical clustering;
    • sense-aware selectional preference models;
    • vector space models instead of WordNet;
    • a broader seed set based on the Master Metaphor List.
  • An extrinsic evaluation within downstream tasks such as MT and opinion mining remains future work.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Natural language processing

Topics: Idioms & multiword expressions

Institutions

🎓University of CambridgeCambridge · United Kingdom119 works · 110 950 citations

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.1162/coli_a_00124

@article{shutova2013statistical,
  title = {{Statistical Metaphor Processing}},
  author = {Shutova, Ekaterina and Teufel, Simone and Korhonen, Anna},
  journal = {Computational Linguistics},
  year = {2013},
  doi = {10.1162/coli\_a\_00124},
  url = {https://doi.org/10.1162/coli_a_00124}
}

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