Paper Info
Title
Enhancement Of Target-Oriented Opinion Words Extraction With Multiview-Trained Machine Reading Comprehension Model
Abstract
Target-oriented opinion words extraction (TOWE) seeks to identify opinion expressions oriented to a specific target, and it is a crucial step toward fine-grained opinion mining. Recent neural networks have achieved significant success in this task by building target-aware representations. However, there are still two limitations of these methods that hinder the progress of TOWE. Mainstream approaches typically utilize position indicators to mark the given target, which is a naive strategy and lacks task-specific semantic meaning. Meanwhile, the annotated target-opinion pairs contain rich latent structural knowledge from multiple perspectives, but existing methods only exploit the TOWE view. To tackle these issues, we formulate the TOWE task as a question answering (QA) problem and leverage a machine reading comprehension (MRC) model trained with a multiview paradigm to extract targeted opinions. Specifically, we introduce a template-based pseudo-question generation method and utilize deep attention interaction to build target-aware context representations and extract related opinion words. To take advantage of latent structural correlations, we further cast the opinion-target structure into three distinct yet correlated views and leverage meta-learning to aggregate common knowledge among them to enhance the TOWE task. We evaluate the proposed model on four benchmark datasets, and our method achieves new state-of-the-art results. Extensional experiments have shown that the pipeline method with our approach could surpass existing opinion pair extraction models, including joint methods that are usually believed to work better.
Year
DOI
Venue
2021
10.1155/2021/6645871
COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE
DocType
Volume
ISSN
Journal
2021
1687-5265
Citations 
PageRank 
References 
0
0.34
23
Authors
6
Name
Order
Citations
PageRank
Jingyuan Zhang165360.53
Zequn Zhang221.38
Zhi Guo300.68
Li Jin400.34
Kang Liu5154289.33
Qing Liu6312.95