Idiom Type Identification with Smoothed Lexical Features and a Maximum Margin Classifier

Giancarlo Salton, Robert Ross, John Kelleher

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Abstract
In our work we address limitations in the state-of-the-art in idiom type identification. We investigate different approaches for a lexical fixedness metric, a component of the state-of the-art model. We also show that our Machine Learning based approach to the idiom type identification task achieves an F1-score of 0.85, an improvement of 11 points over the state-of the-art.
Anthology ID:
R17-1083
Volume:
Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017
Month:
September
Year:
2017
Address:
Varna, Bulgaria
Editors:
Ruslan Mitkov, Galia Angelova
Venue:
RANLP
SIG:
Publisher:
INCOMA Ltd.
Note:
Pages:
642–651
Language:
URL:
https://doi.org/10.26615/978-954-452-049-6_083
DOI:
10.26615/978-954-452-049-6_083
Bibkey:
Cite (ACL):
Giancarlo Salton, Robert Ross, and John Kelleher. 2017. Idiom Type Identification with Smoothed Lexical Features and a Maximum Margin Classifier. In Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017, pages 642–651, Varna, Bulgaria. INCOMA Ltd..
Cite (Informal):
Idiom Type Identification with Smoothed Lexical Features and a Maximum Margin Classifier (Salton et al., RANLP 2017)
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https://doi.org/10.26615/978-954-452-049-6_083
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