{"title":"更好的关键词提取","authors":"Shihua Xu, Fang Kong","doi":"10.1109/IALP.2015.7451561","DOIUrl":null,"url":null,"abstract":"Automatic keyword extraction is the task to identify a small set of keywords from a given document that can describe the meaning of the document. It plays an important role in information retrieval. In this paper, a clustering-based approach to do this task is proposed. And the impacts of keyword length, the window size of centroid on the performance of AKE system are discussed. Then by introducing keyword length constraint and extending the number of centroid of every cluster, the performance of our AKE system is improved by 7.5% in F-score.","PeriodicalId":256927,"journal":{"name":"2015 International Conference on Asian Language Processing (IALP)","volume":"256 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Toward better keywords extraction\",\"authors\":\"Shihua Xu, Fang Kong\",\"doi\":\"10.1109/IALP.2015.7451561\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Automatic keyword extraction is the task to identify a small set of keywords from a given document that can describe the meaning of the document. It plays an important role in information retrieval. In this paper, a clustering-based approach to do this task is proposed. And the impacts of keyword length, the window size of centroid on the performance of AKE system are discussed. Then by introducing keyword length constraint and extending the number of centroid of every cluster, the performance of our AKE system is improved by 7.5% in F-score.\",\"PeriodicalId\":256927,\"journal\":{\"name\":\"2015 International Conference on Asian Language Processing (IALP)\",\"volume\":\"256 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 International Conference on Asian Language Processing (IALP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IALP.2015.7451561\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 International Conference on Asian Language Processing (IALP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IALP.2015.7451561","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Automatic keyword extraction is the task to identify a small set of keywords from a given document that can describe the meaning of the document. It plays an important role in information retrieval. In this paper, a clustering-based approach to do this task is proposed. And the impacts of keyword length, the window size of centroid on the performance of AKE system are discussed. Then by introducing keyword length constraint and extending the number of centroid of every cluster, the performance of our AKE system is improved by 7.5% in F-score.
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