PGCD: a position-guied contributive distribution unit for aspect based sentiment analysis

• URL: http://arxiv.org/abs/2108.05098v1
• Date: Wed, 11 Aug 2021 08:43:13 GMT
• Title: PGCD: a position-guied contributive distribution unit for aspect based sentiment analysis
• Authors: Zijian Zhang, Chenxin Zhang, Qin Liu, Hongming Zhu, Jiangfeng Li
• Abstract summary: We propose a Position-Guided Contributive Distribution (PGCD) unit. It achieves a position-dependent contributive pattern and generates aspect-related statement feature for ABSA task.
• Score: 3.3205853660267635
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Aspect based sentiment analysis (ABSA), exploring sentim- ent polarity of aspect-given sentence, has drawn widespread applications in social media and public opinion. Previously researches typically derive aspect-independent representation by sentence feature generation only depending on text data. In this paper, we propose a Position-Guided Contributive Distribution (PGCD) unit. It achieves a position-dependent contributive pattern and generates aspect-related statement feature for ABSA task. Quoted from Shapley Value, PGCD can gain position-guided contextual contribution and enhance the aspect-based representation. Furthermore, the unit can be used for improving effects on multimodal ABSA task, whose datasets restructured by ourselves. Extensive experiments on both text and text-audio level using dataset (SemEval) show that by applying the proposed unit, the mainstream models advance performance in accuracy and F1 score.

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