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 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% F1 for MWE identification.
Anthology ID:
Q14-1016
Volume:
Transactions of the Association for Computational Linguistics, Volume 2
Month:
Year:
2014
Address:
Cambridge, MA
Editors:
Dekang Lin, Michael Collins, Lillian Lee
Venue:
TACL
SIG:
Publisher:
MIT Press
Note:
Pages:
193–206
Language:
URL:
https://aclanthology.org/Q14-1016
DOI:
10.1162/tacl_a_00176
Bibkey:
Cite (ACL):
Nathan Schneider, Emily Danchik, Chris Dyer, and Noah A. Smith. 2014. Discriminative Lexical Semantic Segmentation with Gaps: Running the MWE Gamut. Transactions of the Association for Computational Linguistics, 2:193–206.
Cite (Informal):
Discriminative Lexical Semantic Segmentation with Gaps: Running the MWE Gamut (Schneider et al., TACL 2014)
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PDF:
https://aclanthology.org/Q14-1016.pdf