Improving Aspect Term Extraction With Bidirectional Dependency Tree Representation

Aspect term extraction is one of the important subtasks in aspect-based sentiment analysis. Previous studies have shown that using dependency tree structure representation is promising for this task. However, most dependency tree structures involve only one directional propagation on the dependency...

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Bibliographic Details
Published in:IEEE/ACM transactions on audio, speech, and language processing speech, and language processing, 2019-07, Vol.27 (7), p.1201-1212
Main Authors: Huaishao Luo, Tianrui Li, Bing Liu, Bin Wang, Unger, Herwig
Format: Article
Language:English
Subjects:
Aspect term extraction
Browsers
Computer architecture
Data mining
Dependence
dependency syntactic tree
Feature extraction
Logic gates
Propagation
recursive neural network
Representations
sentiment analysis
Software
Syntactics
Task analysis
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Summary:Aspect term extraction is one of the important subtasks in aspect-based sentiment analysis. Previous studies have shown that using dependency tree structure representation is promising for this task. However, most dependency tree structures involve only one directional propagation on the dependency tree. In this paper, we first propose a novel bidirectional dependency tree network to extract dependency structure features from the given sentences. The key idea is to explicitly incorporate both representations gained separately from the bottom-up and top-down propagation on the given dependency syntactic tree. An end-to-end framework is then developed to integrate the embedded representations and BiLSTM plus CRF to learn both tree-structured and sequential features to solve the aspect term extraction problem. Experimental results demonstrate that the proposed model outperforms state-of-the-art baseline models on four benchmark SemEval datasets.
ISSN:2329-9290
2329-9304
DOI:10.1109/TASLP.2019.2913094