{"title":"基于关键词的句子相似度度量方法","authors":"Yuanjun Bi, Kai Deng, Jinxing Cheng","doi":"10.1145/3091478.3098878","DOIUrl":null,"url":null,"abstract":"In this paper, a sentence similarity computing approach based on keywords is presented. First, it extracts the keywords from a sentence. Then the approach computes ranking scores for the keywords. Finally it applies these ranking scores into the sentence similarity computation using the Jaccard similarity coefficient. Experiments on a real word chatterbot system dataset demonstrate that this proposed approach significantly improves the relevance of sentence similarity method up to 30%.","PeriodicalId":165747,"journal":{"name":"Proceedings of the 2017 ACM on Web Science Conference","volume":"129 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":"{\"title\":\"A Keyword-Based Method for Measuring Sentence Similarity\",\"authors\":\"Yuanjun Bi, Kai Deng, Jinxing Cheng\",\"doi\":\"10.1145/3091478.3098878\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, a sentence similarity computing approach based on keywords is presented. First, it extracts the keywords from a sentence. Then the approach computes ranking scores for the keywords. Finally it applies these ranking scores into the sentence similarity computation using the Jaccard similarity coefficient. Experiments on a real word chatterbot system dataset demonstrate that this proposed approach significantly improves the relevance of sentence similarity method up to 30%.\",\"PeriodicalId\":165747,\"journal\":{\"name\":\"Proceedings of the 2017 ACM on Web Science Conference\",\"volume\":\"129 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-06-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"11\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2017 ACM on Web Science Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3091478.3098878\",\"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 of the 2017 ACM on Web Science Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3091478.3098878","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Keyword-Based Method for Measuring Sentence Similarity
In this paper, a sentence similarity computing approach based on keywords is presented. First, it extracts the keywords from a sentence. Then the approach computes ranking scores for the keywords. Finally it applies these ranking scores into the sentence similarity computation using the Jaccard similarity coefficient. Experiments on a real word chatterbot system dataset demonstrate that this proposed approach significantly improves the relevance of sentence similarity method up to 30%.
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