{"type":"ScholarlyArticle","title":"Automated Rule Selection for Aspect Extraction in Opinion Mining","authors":[{"name":"Qian Liu","slug":"qian-liu","given":"Qian","family":"Liu","affiliation":"School of Computer Science and Engineering , Southeast University , China; ,; Key Laboratory of Computer Network and Information Integration ( Southeast University) Ministry of Education , China"},{"name":"Zhiqiang Gao","slug":"zhiqiang-gao","given":"Zhiqiang","family":"Gao","affiliation":"School of Computer Science and Engineering , Southeast University , China; ,; Key Laboratory of Computer Network and Information Integration ( Southeast University) Ministry of Education , China"},{"name":"Bing Liu","slug":"bing-liu","given":"Bing","family":"Liu","affiliation":"Department of Computer Science , University of Illinois at Chicago , USA"},{"name":"Yuanlin Zhang","slug":"yuanlin-zhang","given":"Yuanlin","family":"Zhang","affiliation":"Department of Computer Science , Texas Tech University , USA"}],"abstract":"Aspect extraction aims to extract fine-grained opinion targets from opinion texts. Recent work has shown that the syntactical approach, which employs rules about grammar dependency relations between opinion words and aspects, performs quite well. This approach is highly desirable in practice because it is unsupervised and domain independent. However, the rules need to be carefully selected and tuned manually so as not to produce too many errors. Although it is easy to evaluate the accuracy of each rule automatically, it is not easy to select a set of rules that produces the best overall result due to the overlapping coverage of the rules. In this paper, we propose a novel method to select an effective set of rules. To our knowledge, this is the first work that selects rules automatically. Our experiment results show that the proposed method can select a subset of a given rule set to achieve significantly better results than the full rule set and the existing state-of-the-art CRF-based supervised method.","doi":null,"url":"https://ijcai.org/proceedings/2015/186","pdf":"https://ijcai.org/proceedings/2015/0186.pdf","markdown":"https://ijcai.org/proceedings/2015/186.md","bibtex":"https://ijcai.org/proceedings/2015/bibtex/186","volume":{"slug":"2015","title":"Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2015","url":"https://ijcai.org/proceedings/2015/","year":2015,"location":"Buenos Aires, Argentina","dates":"25–31 July 2015","editors":"Qiang Yang and Michael Wooldridge","isbn":"978-1-57735-738-4","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":"Natural Language Processing","track":"Main Track","number":186,"pages":{"first":1291,"last":1296},"keywords":[],"videos":[],"references":[{"position":1,"raw":"[Brody and Elhadad, 2010] Samuel Brody and Noemie Elhadad. 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