Deep-BGT at PARSEME Shared Task 2018: Bidirectional LSTM-CRF Model for Verbal Multiword Expression Identification

Gözde Berk, Berna Erden, Tunga Güngör


Abstract
This paper describes the Deep-BGT system that participated to the PARSEME shared task 2018 on automatic identification of verbal multiword expressions (VMWEs). Our system is language-independent and uses the bidirectional Long Short-Term Memory model with a Conditional Random Field layer on top (bidirectional LSTM-CRF). To the best of our knowledge, this paper is the first one that employs the bidirectional LSTM-CRF model for VMWE identification. Furthermore, the gappy 1-level tagging scheme is used for discontiguity and overlaps. Our system was evaluated on 10 languages in the open track and it was ranked the second in terms of the general ranking metric.
Anthology ID:
W18-4927
Volume:
Proceedings of the Joint Workshop on Linguistic Annotation, Multiword Expressions and Constructions (LAW-MWE-CxG-2018)
Month:
August
Year:
2018
Address:
Santa Fe, New Mexico, USA
Venue:
LAW
SIGs:
SIGLEX | SIGANN
Publisher:
Association for Computational Linguistics
Note:
Pages:
248–253
Language:
URL:
https://aclanthology.org/W18-4927
DOI:
Bibkey:
Cite (ACL):
Gözde Berk, Berna Erden, and Tunga Güngör. 2018. Deep-BGT at PARSEME Shared Task 2018: Bidirectional LSTM-CRF Model for Verbal Multiword Expression Identification. In Proceedings of the Joint Workshop on Linguistic Annotation, Multiword Expressions and Constructions (LAW-MWE-CxG-2018), pages 248–253, Santa Fe, New Mexico, USA. Association for Computational Linguistics.
Cite (Informal):
Deep-BGT at PARSEME Shared Task 2018: Bidirectional LSTM-CRF Model for Verbal Multiword Expression Identification (Berk et al., LAW 2018)
Copy Citation:
PDF:
https://aclanthology.org/W18-4927.pdf
Code
 deep-bgt/Deep-BGT