{"title":"Question Answering System with Enhancing Sentence Embedding","authors":"Hongliang Wang, XinXin Lu","doi":"10.1109/ICTech55460.2022.00109","DOIUrl":null,"url":null,"abstract":"In order to improve the semantic understanding of the input question in the question answering system, a question answering system based on knowledge representation is constructed, which is composed of named entity recognition and question matching. The named entity recognition method based on Bert+BiLSTM+CRF is used, and the BGCNN model proposed in this paper is used for question matching. BGCNN is a model combining Bert, neural network and Siamese network. The average F1 value of the system on the financial data set is 0.9007, which is not a small improvement compared with the previous model.","PeriodicalId":290836,"journal":{"name":"2022 11th International Conference of Information and Communication Technology (ICTech))","volume":"25 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 11th International Conference of Information and Communication Technology (ICTech))","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICTech55460.2022.00109","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
In order to improve the semantic understanding of the input question in the question answering system, a question answering system based on knowledge representation is constructed, which is composed of named entity recognition and question matching. The named entity recognition method based on Bert+BiLSTM+CRF is used, and the BGCNN model proposed in this paper is used for question matching. BGCNN is a model combining Bert, neural network and Siamese network. The average F1 value of the system on the financial data set is 0.9007, which is not a small improvement compared with the previous model.
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