Idiom Type Identification with Smoothed Lexical Features and a Maximum Margin Classifier
20388Authors
nan
Abstract
AbstractIn 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.
Keywords
Full Content
nan
Labeled Text
AbstractIn 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.
Publication Details
- Paper ID:
- 20388
- Year:
- 2017
- Publication:
- TIPSTER
- Publication Type:
- conf
- DOI:
- nan