{"title":"Extracting opinion sentence by combination of SVM and syntactic templates","authors":"Bo Zhang, Yanquan Zhou, Yu Mao","doi":"10.1109/NLPKE.2010.5587835","DOIUrl":null,"url":null,"abstract":"This paper presents a combined method of syntactic structure, dependency relation and SVM classifier to extract opinion sentences. At first, we use the syntactic structure templates with high confidence summarized artificially and the dependency relation templates with high precision obtained by a dependency relation extraction algorithm to tag sentences as opinion sentence. Then we input the remaining test data to a trained SVM classifier which is created by a rigorous process of feature selection. Eventually the combined method performed well, achieving 92.6% recall with 85.5% precision.","PeriodicalId":259975,"journal":{"name":"Proceedings of the 6th International Conference on Natural Language Processing and Knowledge Engineering(NLPKE-2010)","volume":"29 2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 6th International Conference on Natural Language Processing and Knowledge Engineering(NLPKE-2010)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NLPKE.2010.5587835","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5
Abstract
This paper presents a combined method of syntactic structure, dependency relation and SVM classifier to extract opinion sentences. At first, we use the syntactic structure templates with high confidence summarized artificially and the dependency relation templates with high precision obtained by a dependency relation extraction algorithm to tag sentences as opinion sentence. Then we input the remaining test data to a trained SVM classifier which is created by a rigorous process of feature selection. Eventually the combined method performed well, achieving 92.6% recall with 85.5% precision.