Identification of Ambiguous Multiword Expressions Using Sequence Models and Lexical Resources

Manon Scholivet, Carlos Ramisch


Abstract
We present a simple and efficient tagger capable of identifying highly ambiguous multiword expressions (MWEs) in French texts. It is based on conditional random fields (CRF), using local context information as features. We show that this approach can obtain results that, in some cases, approach more sophisticated parser-based MWE identification methods without requiring syntactic trees from a treebank. Moreover, we study how well the CRF can take into account external information coming from a lexicon.
Anthology ID:
W17-1723
Volume:
Proceedings of the 13th Workshop on Multiword Expressions (MWE 2017)
Month:
April
Year:
2017
Address:
Valencia, Spain
Venue:
MWE
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
167–175
Language:
URL:
https://aclanthology.org/W17-1723
DOI:
10.18653/v1/W17-1723
Bibkey:
Cite (ACL):
Manon Scholivet and Carlos Ramisch. 2017. Identification of Ambiguous Multiword Expressions Using Sequence Models and Lexical Resources. In Proceedings of the 13th Workshop on Multiword Expressions (MWE 2017), pages 167–175, Valencia, Spain. Association for Computational Linguistics.
Cite (Informal):
Identification of Ambiguous Multiword Expressions Using Sequence Models and Lexical Resources (Scholivet & Ramisch, MWE 2017)
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PDF:
https://aclanthology.org/W17-1723.pdf