{"title":"基于句法知识和常识知识适配器的面向方面级情感分类网络","authors":"Guojun Lu, Haibo Yu, Yun Xue, Zhixun Qiu, Weiyu Zhong","doi":"10.1145/3498851.3498985","DOIUrl":null,"url":null,"abstract":"Aspect-level sentiment classification is a most pronounced approach, which is defined as an automated technique to extract significant information from a large number of texts. However, current research still has limitations in ALSC tasks (e.g. accuracy of dependency parsing and overlook of commonsense knowledge). In this work, we propose a syntactic knowledge and commonsense knowledge adapter based network, which deals with the position information, syntactic structure and external knowledge, respectively. The performance of our model is evaluated on the three benchmark datasets. Experimental results demonstrate that our model is a best alternative in ALSC tasks compared with the state-of-the-art methods.","PeriodicalId":89230,"journal":{"name":"Proceedings. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology","volume":"33 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2021-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"SCAN:Syntactic Knowledge and Commonsense Knowledge Adapter Based Network for Aspect-level Sentiment Classification\",\"authors\":\"Guojun Lu, Haibo Yu, Yun Xue, Zhixun Qiu, Weiyu Zhong\",\"doi\":\"10.1145/3498851.3498985\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Aspect-level sentiment classification is a most pronounced approach, which is defined as an automated technique to extract significant information from a large number of texts. However, current research still has limitations in ALSC tasks (e.g. accuracy of dependency parsing and overlook of commonsense knowledge). In this work, we propose a syntactic knowledge and commonsense knowledge adapter based network, which deals with the position information, syntactic structure and external knowledge, respectively. The performance of our model is evaluated on the three benchmark datasets. Experimental results demonstrate that our model is a best alternative in ALSC tasks compared with the state-of-the-art methods.\",\"PeriodicalId\":89230,\"journal\":{\"name\":\"Proceedings. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology\",\"volume\":\"33 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-12-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3498851.3498985\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3498851.3498985","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
SCAN:Syntactic Knowledge and Commonsense Knowledge Adapter Based Network for Aspect-level Sentiment Classification
Aspect-level sentiment classification is a most pronounced approach, which is defined as an automated technique to extract significant information from a large number of texts. However, current research still has limitations in ALSC tasks (e.g. accuracy of dependency parsing and overlook of commonsense knowledge). In this work, we propose a syntactic knowledge and commonsense knowledge adapter based network, which deals with the position information, syntactic structure and external knowledge, respectively. The performance of our model is evaluated on the three benchmark datasets. Experimental results demonstrate that our model is a best alternative in ALSC tasks compared with the state-of-the-art methods.