{"type":"ScholarlyArticle","title":"Deep Mask Memory Network with Semantic Dependency and Context Moment for Aspect Level Sentiment Classification","authors":[{"name":"Peiqin Lin","slug":"peiqin-lin","given":"Peiqin","family":"Lin","affiliation":"School of Data and Computer Science , Sun Yat-sen University , Guangzhou , China"},{"name":"Meng Yang","slug":"meng-yang","given":"Meng","family":"Yang","affiliation":"School of Data and Computer Science , Sun Yat-sen University , Guangzhou , China; Key Laboratory of Machine Intelligence and Advanced Computing(SYSU) , Ministry of Education"},{"name":"Jianhuang Lai","slug":"jianhuang-lai","given":"Jianhuang","family":"Lai","affiliation":"School of Data and Computer Science , Sun Yat-sen University , Guangzhou , China"}],"abstract":"Aspect level sentiment classification aims at identifying the sentiment of each aspect term in a sentence. Deep memory networks often use location information between context word and aspect to generate the memory. Although improved results are achieved, the relation information among aspects in the same sentence is ignored and the word location can't bring enough and accurate information for the analysis on the aspect sentiment. In this paper, we propose a novel framework for aspect level sentiment classification, deep mask memory network with semantic dependency and context moment (DMMN-SDCM), which integrates semantic parsing information of the aspect and the inter-aspect relation information into deep memory network. With the designed attention mechanism based on semantic dependency information, different parts of the context memory in different computational layers are selected and useful inter-aspect information in the same sentence is exploited for the desired aspect. To make full use of the inter-aspect relation information, we also jointly learn a context moment learning task, which aims to learn the sentiment distribution of the entire sentence for providing a background for the desired aspect. We examined the merit of our model on SemEval 2014 Datasets, and the experimental results show that our model achieves a state-of-the-art performance.","doi":"10.24963/ijcai.2019/707","url":"https://ijcai.org/proceedings/2019/707","pdf":"https://ijcai.org/proceedings/2019/0707.pdf","markdown":"https://ijcai.org/proceedings/2019/707.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/707","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":707,"pages":{"first":5088,"last":5094},"keywords":["Machine Learning Applications: Applications of Supervised Learning","Natural Language Processing: Natural Language Processing"],"videos":[],"references":[{"position":1,"raw":"[Bahdanau et al., 2014] Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 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