{"title":"基于标签映射的实体属性提取方法研究","authors":"Huilin Liu, Cheng Chen, Liwei Zhang, Guoren Wang","doi":"10.1109/PIC.2010.5687859","DOIUrl":null,"url":null,"abstract":"With the rapid development of new media, such as computer and Internet, extract valuable entity attribute information from Web text can be significant. Aiming at this problem, this paper puts forward SALmap, this model calls seed method at first, which will create common candidate attribute label sets by defining data format rules. Then we construct the mapping relationship between the attributes and the labels using attribute value information and the maximum entropy model, and label the entity instance as well. Finally, hidden Markov model is applied to the relevant entity attribute extraction. Experiments prove SALmap model can significantly improve the precision and performance of entity attribute extraction.","PeriodicalId":142910,"journal":{"name":"2010 IEEE International Conference on Progress in Informatics and Computing","volume":"298 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"The research of label-mapping-based entity attribute extraction\",\"authors\":\"Huilin Liu, Cheng Chen, Liwei Zhang, Guoren Wang\",\"doi\":\"10.1109/PIC.2010.5687859\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"With the rapid development of new media, such as computer and Internet, extract valuable entity attribute information from Web text can be significant. Aiming at this problem, this paper puts forward SALmap, this model calls seed method at first, which will create common candidate attribute label sets by defining data format rules. Then we construct the mapping relationship between the attributes and the labels using attribute value information and the maximum entropy model, and label the entity instance as well. Finally, hidden Markov model is applied to the relevant entity attribute extraction. Experiments prove SALmap model can significantly improve the precision and performance of entity attribute extraction.\",\"PeriodicalId\":142910,\"journal\":{\"name\":\"2010 IEEE International Conference on Progress in Informatics and Computing\",\"volume\":\"298 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 IEEE International Conference on Progress in Informatics and Computing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/PIC.2010.5687859\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 IEEE International Conference on Progress in Informatics and Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PIC.2010.5687859","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The research of label-mapping-based entity attribute extraction
With the rapid development of new media, such as computer and Internet, extract valuable entity attribute information from Web text can be significant. Aiming at this problem, this paper puts forward SALmap, this model calls seed method at first, which will create common candidate attribute label sets by defining data format rules. Then we construct the mapping relationship between the attributes and the labels using attribute value information and the maximum entropy model, and label the entity instance as well. Finally, hidden Markov model is applied to the relevant entity attribute extraction. Experiments prove SALmap model can significantly improve the precision and performance of entity attribute extraction.