{"type":"ScholarlyArticle","title":"Aspect Term Extraction with History Attention and Selective Transformation","authors":[{"name":"Xin Li","slug":"xin-li","given":"Xin","family":"Li","affiliation":"Key Laboratory of High Confidence Software Technologies , Ministry of Education (CUHK Sub-Lab) , Dept of Systems Engineering & Engineering Management , Chinese University of Hong Kong"},{"name":"Lidong Bing","slug":"lidong-bing","given":"Lidong","family":"Bing","affiliation":"Tencent AI Lab , Shenzhen , China"},{"name":"Piji Li","slug":"piji-li","given":"Piji","family":"Li","affiliation":"Key Laboratory of High Confidence Software Technologies , Ministry of Education (CUHK Sub-Lab) , Dept of Systems Engineering & Engineering Management , Chinese University of Hong Kong"},{"name":"Wai Lam","slug":"wai-lam","given":"Wai","family":"Lam","affiliation":"Key Laboratory of High Confidence Software Technologies , Ministry of Education (CUHK Sub-Lab) , Dept of Systems Engineering & Engineering Management , Chinese University of Hong Kong"},{"name":"Zhimou Yang","slug":"zhimou-yang","given":"Zhimou","family":"Yang","affiliation":"College of Information Science and Engineering , Northeastern University , China"}],"abstract":"Aspect Term Extraction (ATE), a key sub-task in Aspect-Based Sentiment Analysis, aims to extract explicit aspect expressions from online user reviews. We present a new framework for tackling ATE. It can exploit two useful clues, namely opinion summary and aspect detection history. Opinion summary is distilled from the whole input sentence, conditioned on each current token for aspect prediction, and thus the tailor-made summary can help aspect prediction on this token. On the other hand, the aspect detection history information is distilled from the previous aspect predictions, and it can leverage the coordinate structure and tagging schema constraints to upgrade the aspect prediction. Experimental results over four benchmark datasets clearly demonstrate that our framework can outperform all state-of-the-art methods.","doi":"10.24963/ijcai.2018/583","url":"https://ijcai.org/proceedings/2018/583","pdf":"https://ijcai.org/proceedings/2018/0583.pdf","markdown":"https://ijcai.org/proceedings/2018/583.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/583","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Natural Language Processing","track":"Main track","number":583,"pages":{"first":4194,"last":4200},"keywords":["Natural Language Processing: Natural Language Processing","Natural Language Processing: Sentiment Analysis and Text Mining"],"videos":[],"references":[{"position":1,"raw":"[Bjerva et al., 2016] Johannes Bjerva, Barbara Plank, and Johan Bos. Semantic tagging with deep residual networks. arXiv preprint arXiv:1609.07053, 2016.","title":"Semantic tagging with deep residual networks","authors":"Bjerva","year":"2016","venue":null,"doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Chernyshevich, 2014] Maryna Chernyshevich. Ihs r&d belarus: Cross-domain extraction of product features using crf. In Proc. of SemEval, pages 309-313, 2014.","title":"Ihs r&d belarus: Cross-domain extraction of product features using crf","authors":"Maryna Chernyshevich","year":"2014","venue":"Proc. of SemEval","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Cuong et al., 2014] Nguyen Viet Cuong, Nan Ye, Wee Sun Lee, and Hai Leong Chieu. Conditional random field with high-order dependencies for sequence labeling and segmentation. JMLR, 15(1):981-1009, 2014.","title":"Conditional random field with high-order dependencies for sequence labeling and segmentation","authors":"Nguyen Viet, Cuong, Nan Ye, Wee Sun Lee, Hai Leong Chieu","year":"2014","venue":"JMLR","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Glorot and Bengio, 2010] Xavier Glorot and Yoshua Bengio. Un- derstanding the difficulty of training deep feedforward neural net- works. In Proc. of AISTATS, volume 9, pages 249-256, 2010.","title":"Xavier Glorot and Yoshua Bengio. Understanding the difficulty of training deep feedforward neural networks","authors":"Bengio Glorot","year":"2010","venue":"Proc. of AISTATS","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Graves, 2012] Alex Graves. Supervised sequence labelling. In Supervised Sequence Labelling with Recurrent Neural Networks, pages 5-13. Springer, 2012.","title":"Supervised sequence labelling","authors":"Alex Graves","year":"2012","venue":"Supervised Sequence Labelling with Recurrent Neural Networks","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[He et al., 2016] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proc. of CVPR, pages 770-778, 2016.","title":"Deep residual learning for image recognition","authors":"Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun","year":"2016","venue":"Proc. of CVPR","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Hu and Liu, 2004] Minqing Hu and Bing Liu. Mining and sum- marizing customer reviews. In Proc. of KDD, pages 168-177, 2004.","title":"Mining and summarizing customer reviews","authors":"Liu Hu, Minqing Hu, Bing Liu","year":"2004","venue":"Proc. of KDD","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Kim, 2014] Yoon Kim. Convolutional neural networks for sen- tence classification. In Proc. of EMNLP, pages 1746-1751, 2014.","title":"Convolutional neural networks for sentence classification","authors":"Yoon Kim","year":"2014","venue":"Proc. of EMNLP","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Li and Lam, 2017] Xin Li and Wai Lam. Deep multi-task learning for aspect term extraction with memory interaction. In Proc. of EMNLP, pages 2886-2892, 2017.","title":"Deep multi-task learning for aspect term extraction with memory interaction","authors":"Li, Lam ; ] Xin Li, Wai Lam","year":"2017","venue":"Proc. of EMNLP","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Liu et al., 2012] Kang Liu, Liheng Xu, and Jun Zhao. Opinion target extraction using word-based translation model. In Proc. of EMNLP, pages 1346-1356, 2012.","title":"Opinion target extraction using word-based translation model","authors":"Kang Liu, Liheng Xu, Jun Zhao","year":"2012","venue":"Proc. of EMNLP","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Liu et al., 2013] Kang Liu, Heng Li Xu, Yang Liu, and Jun Zhao. Opinion target extraction using partially-supervised word align- ment model. In Proc. of IJCAI, pages 2134-2140, 2013.","title":"Opinion target extraction using partially-supervised word alignment model","authors":"Kang Liu, Li Heng, Yang Xu, Jun Liu, Zhao","year":"2013","venue":"Proc. of IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2013/315"},{"position":12,"raw":"[Liu et al., 2015] Pengfei Liu, Shafiq Joty, and Helen Meng. Fine- grained opinion mining with recurrent neural networks and word embeddings. In Proc. of EMNLP, pages 1433-1443, 2015.","title":"Finegrained opinion mining with recurrent neural networks and word embeddings","authors":"Pengfei Liu, Shafiq Joty, Helen Meng","year":"2015","venue":"Proc. of EMNLP","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Liu, 2012] Bing Liu. Sentiment analysis and opinion mining. Syn- thesis Lectures on Human Language Technologies, 5(1):1-167, 2012.","title":"Sentiment analysis and opinion mining","authors":"Bing Liu, Liu","year":"2012","venue":"Synthesis Lectures on Human Language Technologies","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Luong et al., 2015] Thang Luong, Hieu Pham, and Christopher D. Manning. Effective approaches to attention-based neural ma- chine translation. In Proc. of EMNLP, pages 1412-1421, 2015.","title":"Effective approaches to attention-based neural machine translation","authors":"Thang Luong, Hieu Pham, Christopher D Manning","year":"2015","venue":"Proc. of EMNLP","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Manek et al., 2016] Asha S Manek, P Deepa Shenoy, M Chandra Mohan, and KR Venugopal. Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and svm classifier. WWWJ, 20:135-154, 2016.","title":"Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and svm classifier","authors":"P Asha S Manek, M Deepa Shenoy, Chandra Mohan, Venugopal","year":"2016","venue":"WWWJ","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Mintz et al., 2009] Mike Mintz, Steven Bills, Rion Snow, and Dan Jurafsky. Distant supervision for relation extraction without la- beled data. In Proc. of ACL, pages 1003-1011, 2009.","title":"Distant supervision for relation extraction without labeled data","authors":"Mike Mintz, Steven Bills, Rion Snow, Dan Jurafsky","year":"2009","venue":"Proc. of ACL","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Pennington et al., 2014] Jeffrey Pennington, Richard Socher, and Christopher Manning. Glove: Global vectors for word represen- tation. In Proc. of EMNLP, pages 1532-1543, 2014.","title":"Glove: Global vectors for word representation","authors":"Jeffrey Pennington, Richard Socher, Christopher Manning","year":"2014","venue":"Proc. of EMNLP","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Pontiki et al., 2014] Maria Pontiki, Dimitris Galanis, John Pavlopoulos, Harris Papageorgiou, Ion Androutsopoulos, and Suresh Manandhar. Semeval-2014 task 4: Aspect based sentiment analysis. In Proc. of SemEval, pages 27-35, 2014.","title":"Semeval-2014 task 4: Aspect based sentiment analysis","authors":"Maria Pontiki, Dimitris Galanis, John Pavlopoulos, Harris Papageorgiou, Ion Androutsopoulos, Suresh Manandhar","year":"2014","venue":"Proc. of SemEval","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Pontiki et al., 2015] Maria Pontiki, Dimitris Galanis, Haris Pa- pageorgiou, Suresh Manandhar, and Ion Androutsopoulos. Semeval-2015 task 12: Aspect based sentiment analysis. In Proc. of SemEval, pages 486-495, 2015.","title":"Semeval-2015 task 12: Aspect based sentiment analysis","authors":"Maria Pontiki, Dimitris Galanis, Haris Papageorgiou, Suresh Manandhar, Ion Androutsopoulos","year":"2015","venue":"Proc. of SemEval","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Pontiki et al., 2016] Maria Pontiki, Dimitris Galanis, and et al. Semeval-2016 task 5: Aspect based sentiment analysis. In Proc. of SemEval, pages 19-30, 2016.","title":"Semeval-2016 task 5: Aspect based sentiment analysis","authors":"Maria Pontiki, Dimitris Galanis","year":"2016","venue":"Proc. of SemEval","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Popescu and Etzioni, 2005] Ana-Maria Popescu and Oren Etzioni. Extracting product features and opinions from reviews. In Proc. of EMNLP, pages 339-346, 2005.","title":"Extracting product features and opinions from reviews","authors":"Etzioni ; Ana-Maria Popescu, Oren Popescu, Etzioni","year":"2005","venue":"Proc. of EMNLP","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Poria et al., 2016] Soujanya Poria, Erik Cambria, and Alexander Gelbukh. Aspect extraction for opinion mining with a deep con- volutional neural network. Knowledge-Based Systems, 108:42- 49, 2016.","title":"Aspect extraction for opinion mining with a deep convolutional neural network","authors":"Poria","year":"2016","venue":"Knowledge-Based Systems","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Qiu et al., 2011] Guang Qiu, Bing Liu, Jiajun Bu, and Chun Chen. Opinion word expansion and target extraction through double propagation. Computational Linguistics, 37(1):9-27, 2011.","title":"Opinion word expansion and target extraction through double propagation","authors":"Guang Qiu, Bing Liu, Jiajun Bu, Chun Chen","year":"2011","venue":"Computational Linguistics","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[San Vicente et al., 2015] Iñaki San Vicente, Xabier Saralegi, and Rodrigo Agerri. Elixa: A modular and flexible absa platform. In Proc. of SemEval, pages 748-752, 2015.","title":"Rodrigo Agerri. Elixa: A modular and flexible absa platform","authors":"San Vicente","year":"2015","venue":"Proc. of SemEval","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Sarawagi et al., 2004] Sunita Sarawagi, William W Cohen, et al. Semi-markov conditional random fields for information extrac- tion. In Proc. of NIPS, pages 1185-1192, 2004.","title":"Semi-markov conditional random fields for information extraction","authors":"Sunita Sarawagi, William W Cohen","year":"2004","venue":"Proc. of NIPS","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Toh and Su, 2016] Zhiqiang Toh and Jian Su. Nlangp at semeval- 2016 task 5: Improving aspect based sentiment analysis using neural network features. In Proc. of SemEval, pages 282-288, 2016.","title":"Nlangp at semeval-2016 task 5: Improving aspect based sentiment analysis using neural network features","authors":"Zhiqiang Toh, Jian Su","year":"2016","venue":"Proc. of SemEval","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Toh and Wang, 2014] Zhiqiang Toh and Wenting Wang. Dlirec: Aspect term extraction and term polarity classification system. In Proc. of SemEval, pages 235-240, 2014.","title":"Dlirec: Aspect term extraction and term polarity classification system","authors":"Zhiqiang Toh, Wenting Wang","year":"2014","venue":"Proc. of SemEval","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Wang et al., 2016] Wenya Wang, Sinno Jialin Pan, Daniel Dahlmeier, and Xiaokui Xiao. Recursive neural conditional random fields for aspect-based sentiment analysis. In Proc. of EMNLP, pages 616-626, 2016.","title":"Recursive neural conditional random fields for aspect-based sentiment analysis","authors":"Wenya Wang, Sinno Jialin Pan, Daniel Dahlmeier, Xiaokui Xiao","year":"2016","venue":"Proc. of EMNLP","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Wang et al., 2017] Wenya Wang, Sinno Jialin Pan, Daniel Dahlmeier, and Xiaokui Xiao. Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In Proc. of AAAI, pages 3316-3322, 2017.","title":"Coupled multi-layer attentions for co-extraction of aspect and opinion terms","authors":"Wenya Wang, Sinno Jialin Pan, Daniel Dahlmeier, Xiaokui Xiao","year":"2017","venue":"Proc. of AAAI","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Wu et al., 2016] Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, et al. Google's neural machine translation system: Bridging the gap between hu- man and machine translation. arXiv preprint arXiv:1609.08144, 2016.","title":"Google's neural machine translation system: Bridging the gap between human and machine translation","authors":"Yonghui Wu, Mike Schuster, Zhifeng Chen, V Quoc, Mohammad Le, Wolfgang Norouzi, Maxim Macherey, Yuan Krikun, Qin Cao, Klaus Gao, Macherey","year":"2016","venue":null,"doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Xu et al., 2015] Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron Courville, Ruslan Salakhudinov, Rich Zemel, and Yoshua Bengio. Show, attend and tell: Neural image caption generation with visual attention. In Proc. of ICML, pages 2048- 2057, 2015.","title":"Show, attend and tell: Neural image caption generation with visual attention","authors":"Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron Courville, Ruslan Salakhudinov, Rich Zemel, Yoshua Bengio","year":"2015","venue":"Proc. of ICML","doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Yin et al., 2016] Yichun Yin, Furu Wei, Li Dong, Kaimeng Xu, Ming Zhang, and Ming Zhou. Unsupervised word and depen- dency path embeddings for aspect term extraction. In Proc. of IJCAI, pages 2979-2985, 2016.","title":"Unsupervised word and dependency path embeddings for aspect term extraction","authors":"Yichun Yin, Furu Wei, Li Dong, Kaimeng Xu, Ming Zhang, Ming Zhou","year":"2016","venue":"Proc. of IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2016/423"}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}