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Aspect and Opinion Term Extraction Using Graph Attention Network

arXiv 2404.19260 ↗First posted 30 Apr 2024cs.CLcited by 0

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Abstract (arXiv)

In this work we investigate the capability of Graph Attention Network for extracting aspect and opinion terms. Aspect and opinion term extraction is posed as a token-level classification task akin to named entity recognition. We use the dependency tree of the input query as additional feature in a Graph Attention Network along with the token and part-of-speech features. We show that the dependency structure is a powerful feature that in the presence of a CRF layer substantially improves the performance and generates the best result on the commonly used datasets from SemEval 2014, 2015 and 2016. We experiment with additional layers like BiLSTM and Transformer in addition to the CRF layer. We also show that our approach works well in the presence of multiple aspects or sentiments in the same query and it is not necessary to modify the dependency tree based on a single aspect as was the original application for sentiment classification.

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References · 20

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8 more
  1. 2019Exploiting BERT for End-to-End Aspect-based Sentiment Analysiscited by 57
  2. 2018A Unified Model for Opinion Target Extraction and Target Sentiment Predictioncited by 56
  3. 2019An Interactive Multi-Task Learning Network for End-to-End Aspect-Based Sentiment Analysiscited by 52
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  6. 2020A Multi-task Learning Framework for Opinion Triplet Extractioncited by 25
  7. 2021PASTE: A Tagging-Free Decoding Framework Using Pointer Networks for Aspect Sentiment Triplet Extractioncited by 18
  8. 2019DOER: Dual Cross-Shared RNN for Aspect Term-Polarity Co-Extractioncited by 17

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