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Idioms: Humans or Machines, It’s All About Context

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Computational Linguistics and Intelligent Text Processing (CICLing 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10761))

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Abstract

Expressions can be ambiguous between idiomatic and literal interpretation depending on the context they occur in (“sales hit the roof” vs “hit the roof of the car”). Previous studies suggest that idiomaticity is not a binary property, but rather a continuum or the so-called “scalar phenomenon” ranging from completely literal to highly idiomatic. This paper reports the results of an experiment in which human annotators rank idiomatic expressions in context on a scale from 1 (literal) to 4 (highly idiomatic). Our experiment supports the hypothesis that idioms fall on a continuum and that one might differentiate between highly idiomatic, mildly idiomatic and weakly idiomatic expressions. In addition, we measure the relative idiomaticity of 11 idiomatic types and compute the correlation between the relative idiomaticity of an expression and the performance of various automatic models for idiom detection. We show that our model, based on the distributional semantics ideas, not only outperforms the previous models, but also positively correlates with the human judgements, which suggests that we are moving in the right direction toward automatic idiom detection.

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Notes

  1. 1.

    Naturally, this requirement does not guarantee a native speaker, but we had not a better option to control for it.

  2. 2.

    We should clarify here that even though all items that we used were already marked as idiomatic in the [10]’s data, we decided to keep the option of ranking them as literal, just in case of a mistake or a different interpretation. Remember that [10]’s dataset is annotated by only two annotators.

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Acknowledgments

This material is based upon work supported by the Air Force Office of Scientific Research under award number FA9550-16-1-0261 and a National Science Foundation grant IIS-1319846.

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Authors and Affiliations

  1. Department of Linguistics, Department of Computer Science, Montclair State University, Montclair, NJ, 07043, USA

    Manali Pradhan, Jing Peng, Anna Feldman & Bianca Wright

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  1. Manali Pradhan
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  2. Jing Peng
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  3. Anna Feldman
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  4. Bianca Wright
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Correspondence to Anna Feldman .

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Editors and Affiliations

  1. CIC, Instituto Politécnico Nacional, Mexico City, Mexico

    Alexander Gelbukh

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Pradhan, M., Peng, J., Feldman, A., Wright, B. (2018). Idioms: Humans or Machines, It’s All About Context. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2017. Lecture Notes in Computer Science(), vol 10761. Springer, Cham. https://doi.org/10.1007/978-3-319-77113-7_23

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  • DOI: https://doi.org/10.1007/978-3-319-77113-7_23

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