A More Fine-Grained Aspect-Sentiment-Opinion Triplet Extraction Task

• URL: http://arxiv.org/abs/2103.15255v1
• Date: Mon, 29 Mar 2021 00:42:51 GMT
• Title: A More Fine-Grained Aspect-Sentiment-Opinion Triplet Extraction Task
• Authors: Fang Wang, Yuncong Li, Wenjun Zhang, Sheng-hua Zhong
• Abstract summary: We introduce a more fine-grained Aspect-Sentiment-Opinion Triplet Extraction Task. The sentiment in a triplet extracted by ASOTE is the sentiment of the aspect term and opinion term pair. We build four datasets for ASOTE based on several popular ABSA benchmarks.
• Score: 19.101354902943154
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Aspect Sentiment Triplet Extraction (ASTE) aims to extract aspect term, sentiment and opinion term triplets from sentences and tries to provide a complete solution for aspect-based sentiment analysis (ABSA). However, some triplets extracted by ASTE are confusing, since the sentiment in a triplet extracted by ASTE is the sentiment that the sentence expresses toward the aspect term rather than the sentiment of the aspect term and opinion term pair. In this paper, we introduce a more fine-grained Aspect-Sentiment-Opinion Triplet Extraction (ASOTE) Task. ASOTE also extracts aspect term, sentiment and opinion term triplets. However, the sentiment in a triplet extracted by ASOTE is the sentiment of the aspect term and opinion term pair. We build four datasets for ASOTE based on several popular ABSA benchmarks. We propose two methods for ASOTE. The first method extracts the opinion terms of an aspect term and predicts the sentiments of the aspect term and opinion term pairs jointly with a unified tag schema. The second method is based on multiple instance learning, which is trained on ASTE datasets, but can also perform the ASOTE task. Experimental results on the four datasets demonstrate the effectiveness of our methods.

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