Aspect-Sentiment-Multiple-Opinion Triplet Extraction
This addresses the problem of incomplete sentiment analysis in natural language processing for applications like review mining, though it is incremental as it builds on the existing ASTE task.
The paper tackles the limitation of Aspect Sentiment Triplet Extraction (ASTE) by introducing a new task called Aspect Sentiment Multiple Opinions Triplet Extraction (ASMOTE), which extracts triplets with all opinions for each aspect to provide complete reasoning for sentiments, and proposes an Aspect-Guided Framework (AGF) that improves performance on multiple datasets.
Aspect Sentiment Triplet Extraction (ASTE) aims to extract aspect term (aspect), sentiment and opinion term (opinion) triplets from sentences and can tell a complete story, i.e., the discussed aspect, the sentiment toward the aspect, and the cause of the sentiment. ASTE is a charming task, however, one triplet extracted by ASTE only includes one opinion of the aspect, but an aspect in a sentence may have multiple corresponding opinions and one opinion only provides part of the reason why the aspect has this sentiment, as a consequence, some triplets extracted by ASTE are hard to understand, and provide erroneous information for downstream tasks. In this paper, we introduce a new task, named Aspect Sentiment Multiple Opinions Triplet Extraction (ASMOTE). ASMOTE aims to extract aspect, sentiment and multiple opinions triplets. Specifically, one triplet extracted by ASMOTE contains all opinions about the aspect and can tell the exact reason that the aspect has the sentiment. We propose an Aspect-Guided Framework (AGF) to address this task. AGF first extracts aspects, then predicts their opinions and sentiments. Moreover, with the help of the proposed Sequence Labeling Attention(SLA), AGF improves the performance of the sentiment classification using the extracted opinions. Experimental results on multiple datasets demonstrate the effectiveness of our approach.