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        <title>Enhancing Aspect Term Extraction with Soft Prototypes</title>
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    <abstract>Aspect term extraction (ATE) aims to extract aspect terms from a review sentence that users have expressed opinions on. Existing studies mostly focus on designing neural sequence taggers to extract linguistic features from the token level. However, since the aspect terms and context words usually exhibit long-tail distributions, these taggers often converge to an inferior state without enough sample exposure. In this paper, we propose to tackle this problem by correlating words with each other through soft prototypes. These prototypes, generated by a soft retrieval process, can introduce global knowledge from internal or external data and serve as the supporting evidence for discovering the aspect terms. Our proposed model is a general framework and can be combined with almost all sequence taggers. Experiments on four SemEval datasets show that our model boosts the performance of three typical ATE methods by a large margin.</abstract>
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%0 Conference Proceedings
%T Enhancing Aspect Term Extraction with Soft Prototypes
%A Chen, Zhuang
%A Qian, Tieyun
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F chen-qian-2020-enhancing
%X Aspect term extraction (ATE) aims to extract aspect terms from a review sentence that users have expressed opinions on. Existing studies mostly focus on designing neural sequence taggers to extract linguistic features from the token level. However, since the aspect terms and context words usually exhibit long-tail distributions, these taggers often converge to an inferior state without enough sample exposure. In this paper, we propose to tackle this problem by correlating words with each other through soft prototypes. These prototypes, generated by a soft retrieval process, can introduce global knowledge from internal or external data and serve as the supporting evidence for discovering the aspect terms. Our proposed model is a general framework and can be combined with almost all sequence taggers. Experiments on four SemEval datasets show that our model boosts the performance of three typical ATE methods by a large margin.
%R 10.18653/v1/2020.emnlp-main.164
%U https://aclanthology.org/2020.emnlp-main.164/
%U https://doi.org/10.18653/v1/2020.emnlp-main.164
%P 2107-2117

Markdown (Informal)

[Enhancing Aspect Term Extraction with Soft Prototypes](https://aclanthology.org/2020.emnlp-main.164/) (Chen & Qian, EMNLP 2020)

• Enhancing Aspect Term Extraction with Soft Prototypes (Chen & Qian, EMNLP 2020)

ACL

• Zhuang Chen and Tieyun Qian. 2020. Enhancing Aspect Term Extraction with Soft Prototypes. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 2107–2117, Online. Association for Computational Linguistics.