Document Type
Conference Proceeding
Publication Date
1-1-2018
Journal / Book Title
Computational Linguistics and Intelligent Text Processing
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.
DOI
10.1007/978-3-319-77113-7_23
Journal ISSN / Book ISBN
Online ISBN978-3-319-77113-7
Montclair State University Digital Commons Citation
Pradhan, Manali; Peng, Jing; Feldman, Anna; and Wright, Bianca, "Idioms: Humans or Machines, It’s All About Context" (2018). Department of Computer Science Faculty Scholarship and Creative Works. 328.
https://digitalcommons.montclair.edu/compusci-facpubs/328
Published Citation
Pradhan, M., Peng, J., Feldman, A., & Wright, B. (2018). Idioms: Humans or machines, it’s all about context. In A. Gelbukh (Ed.), Computational Linguistics and Intelligent Text Processing - 18th International Conference, CICLing 2017, Revised Selected Papers (pp. 291-304). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10761 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-77113-7_23
