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Most Influential NAACL 2019 Paper · 2026-03 edition

Utilizing BERT For Aspect-Based Sentiment Analysis Via Constructing Auxiliary Sentence

Chi Sun; Luyao Huang; Xipeng Qiu,

Venue
Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL) 2019
Recognition
Most Influential NAACL 2019 Paper (Rank No. 12)
Edition
2026-03
Impact factor
7
Certificate ID
1731bf8fbe43a833

Abstract

Aspect-based sentiment analysis (ABSA), which aims to identify fine-grained opinion polarity towards a specific aspect, is a challenging subtask of sentiment analysis (SA). In this paper, we construct an auxiliary sentence from the aspect and convert ABSA to a sentence-pair classification task, such as question answering (QA) and natural language inference (NLI). We fine-tune the pre-trained model from BERT and achieve new state-of-the-art results on SentiHood and SemEval-2014 Task 4 datasets. The source codes are available at https://github.com/HSLCY/ABSA-BERT-pair.

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