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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.
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.