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
- 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
- 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)
- PDF:
- https://doi.org/10.26615/978-954-452-049-6_083