We present a novel representation, evaluation measure, and supervised models for the task of identifying the multiword expressions (MWEs) in a sentence, resulting in a lexical semantic segmentation. Our approach generalizes a standard chunking representation to encode MWEs containing gaps, thereby enabling efficient sequence tagging algorithms for feature-rich discriminative models. Experiments on a new dataset of English web text offer the first linguistically-driven evaluation of MWE identification with truly heterogeneous expression types. Our statistical sequence model greatly outperforms a lookup-based segmentation procedure, achieving nearly 60% F 1 for MWE identification.
CITATION STYLE
Schneider, N., Danchik, E., Dyer, C., & Smith, N. A. (2014). Discriminative Lexical Semantic Segmentation with Gaps: Running the MWE Gamut. Transactions of the Association for Computational Linguistics, 2, 193–206. https://doi.org/10.1162/tacl_a_00176
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