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

TL;DR

This paper introduces a novel sequence-to-sequence relation extraction approach for jointly extracting aspect-polarity pairs in text, addressing limitations of previous methods by capturing relationships and correlations more effectively.

Contribution

It proposes a position- and aspect-aware sequence-to-sequence model that directly generates aspect-polarity pairs as relations, improving extraction performance over existing approaches.

Findings

Outperforms state-of-the-art methods on benchmark datasets

Effectively captures relationships among aspect-polarity pairs

Demonstrates significant improvement in extraction accuracy

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.