{"type":"ScholarlyArticle","title":"Learn to Select via Hierarchical Gate Mechanism for Aspect-Based Sentiment Analysis","authors":[{"name":"Xiangying Ran","slug":"xiangying-ran","given":"Xiangying","family":"Ran","affiliation":"National Key Laboratory for Novel Software Technology , Nanjing University , Nanjing , China Department of Computer Science and Technology ,"},{"name":"Yuanyuan Pan","slug":"yuanyuan-pan","given":"Yuanyuan","family":"Pan","affiliation":"National Key Laboratory for Novel Software Technology , Nanjing University , Nanjing , China Department of Computer Science and Technology ,"},{"name":"Wei Sun","slug":"wei-sun","given":"Wei","family":"Sun","affiliation":"National Key Laboratory for Novel Software Technology , Nanjing University , Nanjing , China Department of Computer Science and Technology ,"},{"name":"Chongjun Wang","slug":"chongjun-wang","given":"Chongjun","family":"Wang","affiliation":"National Key Laboratory for Novel Software Technology , Nanjing University , Nanjing , China Department of Computer Science and Technology ,; Nanjing University , Nanjing , China"}],"abstract":"Aspect-based sentiment analysis (ABSA) is a fine-grained task. Recurrent Neural Network (RNN) model armed with attention mechanism seems a natural fit for this task, and actually it achieves the state-of-the-art performance recently. However, previous attention mechanisms proposed for ABSA may attend irrelevant words and thus downgrade the performance, especially when dealing with long and complex sentences with multiple aspects. In this paper, we propose a novel architecture named Hierarchical Gate Memory Network (HGMN) for ABSA: firstly, we employ the proposed hierarchical gate mechanism to learn to select the related part about the given aspect, which can keep the original sequence structure of sentence at the same time. After that, we apply Convolutional Neural Network (CNN) on the final aspect-specific memory. We conduct extensive experiments on the SemEval 2014 and Twitter dataset, and results demonstrate that our model outperforms attention based state-of-the-art baselines.","doi":"10.24963/ijcai.2019/717","url":"https://ijcai.org/proceedings/2019/717","pdf":"https://ijcai.org/proceedings/2019/0717.pdf","markdown":"https://ijcai.org/proceedings/2019/717.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/717","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Natural Language Processing","track":"Main track","number":717,"pages":{"first":5160,"last":5167},"keywords":["Natural Language Processing: Sentiment Analysis and Text Mining","Natural Language Processing: Natural Language Processing","Natural Language Processing: Text Classification"],"videos":[],"references":[{"position":1,"raw":"[Bahdanau et al., 2014] Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 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