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    <abstract>Opinion entity extraction is a fundamental task in fine-grained opinion mining. Related studies generally extract aspects and/or opinion expressions without recognizing the relations between them. However, the relations are crucial for downstream tasks, including sentiment classification, opinion summarization, etc. In this paper, we explore Aspect-Opinion Pair Extraction (AOPE) task, which aims at extracting aspects and opinion expressions in pairs. To deal with this task, we propose Synchronous Double-channel Recurrent Network (SDRN) mainly consisting of an opinion entity extraction unit, a relation detection unit, and a synchronization unit. The opinion entity extraction unit and the relation detection unit are developed as two channels to extract opinion entities and relations simultaneously. Furthermore, within the synchronization unit, we design Entity Synchronization Mechanism (ESM) and Relation Synchronization Mechanism (RSM) to enhance the mutual benefit on the above two channels. To verify the performance of SDRN, we manually build three datasets based on SemEval 2014 and 2015 benchmarks. Extensive experiments demonstrate that SDRN achieves state-of-the-art performances.</abstract>
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%0 Conference Proceedings
%T Synchronous Double-channel Recurrent Network for Aspect-Opinion Pair Extraction
%A Chen, Shaowei
%A Liu, Jie
%A Wang, Yu
%A Zhang, Wenzheng
%A Chi, Ziming
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F chen-etal-2020-synchronous
%X Opinion entity extraction is a fundamental task in fine-grained opinion mining. Related studies generally extract aspects and/or opinion expressions without recognizing the relations between them. However, the relations are crucial for downstream tasks, including sentiment classification, opinion summarization, etc. In this paper, we explore Aspect-Opinion Pair Extraction (AOPE) task, which aims at extracting aspects and opinion expressions in pairs. To deal with this task, we propose Synchronous Double-channel Recurrent Network (SDRN) mainly consisting of an opinion entity extraction unit, a relation detection unit, and a synchronization unit. The opinion entity extraction unit and the relation detection unit are developed as two channels to extract opinion entities and relations simultaneously. Furthermore, within the synchronization unit, we design Entity Synchronization Mechanism (ESM) and Relation Synchronization Mechanism (RSM) to enhance the mutual benefit on the above two channels. To verify the performance of SDRN, we manually build three datasets based on SemEval 2014 and 2015 benchmarks. Extensive experiments demonstrate that SDRN achieves state-of-the-art performances.
%R 10.18653/v1/2020.acl-main.582
%U https://aclanthology.org/2020.acl-main.582
%U https://doi.org/10.18653/v1/2020.acl-main.582
%P 6515-6524

Markdown (Informal)

[Synchronous Double-channel Recurrent Network for Aspect-Opinion Pair Extraction](https://aclanthology.org/2020.acl-main.582) (Chen et al., ACL 2020)

• Synchronous Double-channel Recurrent Network for Aspect-Opinion Pair Extraction (Chen et al., ACL 2020)

ACL

• Shaowei Chen, Jie Liu, Yu Wang, Wenzheng Zhang, and Ziming Chi. 2020. Synchronous Double-channel Recurrent Network for Aspect-Opinion Pair Extraction. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 6515–6524, Online. Association for Computational Linguistics.