First Target and Opinion then Polarity: Enhancing Target-opinion Correlation for Aspect Sentiment Triplet Extraction

• URL: http://arxiv.org/abs/2102.08549v1
• Date: Wed, 17 Feb 2021 03:28:17 GMT
• Title: First Target and Opinion then Polarity: Enhancing Target-opinion Correlation for Aspect Sentiment Triplet Extraction
• Authors: Lianzhe Huang, Peiyi Wang, Sujian Li, Tianyu Liu, Xiaodong Zhang, Zhicong Cheng, Dawei Yin, Houfeng Wang
• Abstract summary: Aspect Sentiment Triplet Extraction (ASTE) aims to extract triplets from a sentence, including target entities, associated sentiment polarities, and opinion spans which rationalize the polarities. Existing methods are short on building correlation between target-opinion pairs, and neglect the mutual interference among different sentiment triplets. We propose a novel two-stage method which enhances the correlation between targets and opinions through sequence tagging.
• Score: 45.82241446769157
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Aspect Sentiment Triplet Extraction (ASTE) aims to extract triplets from a sentence, including target entities, associated sentiment polarities, and opinion spans which rationalize the polarities. Existing methods are short on building correlation between target-opinion pairs, and neglect the mutual interference among different sentiment triplets. To address these issues, we propose a novel two-stage method which enhances the correlation between targets and opinions: at stage one, we extract targets and opinions through sequence tagging; then we insert a group of artificial tags named Perceivable Pair, which indicate the span of the target and the opinion, into the sequence to establish correlation for each candidate target-opinion pair. Meanwhile, we reduce the mutual interference between triplets by restricting tokens' attention field. Finally, the polarity is identified according to the representation of the Perceivable Pair. We conduct experiments on four datasets, and the experimental results show that our model outperforms the state-of-the-art methods.

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