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

Authors

Giancarlo Salton, Technological University DublinFollow
Robert J. Ross, Technological University DublinFollow
John D. Kelleher, Technological University DublinFollow

Document Type

Conference Paper

Rights

Available under a Creative Commons Attribution Non-Commercial Share Alike 4.0 International Licence

Disciplines

Computer Sciences

Publication Details

International Conference Recent Advances in Natural Language Processing, Bulgaria, 2017.

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.

DOI

https://doi.org/10.21427/D7DF1S

Recommended Citation

Salton, G., Ross, R., Kelleher, J. (2017) Idiom Type Identification with Smoothed Lexical Features and a Maximum Margin Classifier. International Conference Recent Advances in Natural Language Processing, Bulgaria, 2017.

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.