Extracting all Aspect-polarity Pairs Jointly in a Text with Relation Extraction Approach
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by
Lingmei Bu, Li Chen, Yongmei Lu, Zhonghua Yu
2021
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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