Learning with Structured Representations for Negation Scope Extraction

Hao Li, Wei Lu


Abstract
We report an empirical study on the task of negation scope extraction given the negation cue. Our key observation is that certain useful information such as features related to negation cue, long-distance dependencies as well as some latent structural information can be exploited for such a task. We design approaches based on conditional random fields (CRF), semi-Markov CRF, as well as latent-variable CRF models to capture such information. Extensive experiments on several standard datasets demonstrate that our approaches are able to achieve better results than existing approaches reported in the literature.
Anthology ID:
P18-2085
Volume:
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
Month:
July
Year:
2018
Address:
Melbourne, Australia
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
533–539
Language:
URL:
https://www.aclweb.org/anthology/P18-2085
DOI:
10.18653/v1/P18-2085
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PDF:
http://aclanthology.lst.uni-saarland.de/P18-2085.pdf
Note:
 P18-2085.Notes.pdf
Poster:
 P18-2085.Poster.pdf