Discriminative Lexical Semantic Segmentation with Gaps: Running the MWE Gamut

Nathan Schneider, Emily Danchik, Chris Dyer, Noah A. Smith

Abstract


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 a subset of projective 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 60% F1 for MWE identification.

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