Extracting all Aspect-polarity Pairs Jointly in a Text with Relation Extraction Approach

• URL: http://arxiv.org/abs/2109.00256v1
• Date: Wed, 1 Sep 2021 09:00:39 GMT
• Title: Extracting all Aspect-polarity Pairs Jointly in a Text with Relation Extraction Approach
• Authors: Lingmei Bu, Li Chen, Yongmei Lu and Zhonghua Yu
• Abstract summary: We propose to generate aspect-polarity pairs directly from a text with relation extraction technology. We present a position- and aspect-aware sequence2sequence model for joint extraction of aspect-polarity pairs.
• Score: 6.844982778392037
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Extracting aspect-polarity pairs from texts is an important task of fine-grained sentiment analysis. While the existing approaches to this task have gained many progresses, they are limited at capturing relationships among aspect-polarity pairs in a text, thus degrading the extraction performance. Moreover, the existing state-of-the-art approaches, namely token-based se-quence tagging and span-based classification, have their own defects such as polarity inconsistency resulted from separately tagging tokens in the former and the heterogeneous categorization in the latter where aspect-related and polarity-related labels are mixed. In order to remedy the above defects, in-spiring from the recent advancements in relation extraction, we propose to generate aspect-polarity pairs directly from a text with relation extraction technology, regarding aspect-pairs as unary relations where aspects are enti-ties and the corresponding polarities are relations. Based on the perspective, we present a position- and aspect-aware sequence2sequence model for joint extraction of aspect-polarity pairs. The model is characterized with its ability to capture not only relationships among aspect-polarity pairs in a text through the sequence decoding, but also correlations between an aspect and its polarity through the position- and aspect-aware attentions. The experi-ments performed on three benchmark datasets demonstrate that our model outperforms the existing state-of-the-art approaches, making significant im-provement over them.

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