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Vol. 2 (2014)

TACL approved

Discriminative Lexical Semantic Segmentation with Gaps: Running the MWE Gamut

Published 2014-04-30

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

Nathan Schneider
Carnegie Mellon University

Emily Danchik
Carnegie Mellon University

Chris Dyer
Carnegie Mellon University

Noah A. Smith
Carnegie Mellon University

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.
PDF (Presented at ACL 2014)