Context-aware Embedding for Targeted Aspect-based Sentiment Analysis

Bin Liang, Jiachen Du, Ruifeng Xu, Binyang Li, Hejiao Huang


Abstract
Attention-based neural models were employed to detect the different aspects and sentiment polarities of the same target in targeted aspect-based sentiment analysis (TABSA). However, existing methods do not specifically pre-train reasonable embeddings for targets and aspects in TABSA. This may result in targets or aspects having the same vector representations in different contexts and losing the context-dependent information. To address this problem, we propose a novel method to refine the embeddings of targets and aspects. Such pivotal embedding refinement utilizes a sparse coefficient vector to adjust the embeddings of target and aspect from the context. Hence the embeddings of targets and aspects can be refined from the highly correlative words instead of using context-independent or randomly initialized vectors. Experiment results on two benchmark datasets show that our approach yields the state-of-the-art performance in TABSA task.
Anthology ID:
P19-1462
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4678–4683
Language:
URL:
https://www.aclweb.org/anthology/P19-1462
DOI:
10.18653/v1/P19-1462
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/P19-1462.pdf