Kun Li, Junsheng Zhang, Changqing Yao, Chongde Shi
{"title":"文本关系自动提取研究进展","authors":"Kun Li, Junsheng Zhang, Changqing Yao, Chongde Shi","doi":"10.1109/IIKI.2016.58","DOIUrl":null,"url":null,"abstract":"Relation extraction is an important task for understanding text. In the big data era, automatic relation extraction from unstructured texts is urgently needed for structured information organization and information analysis. In this paper, we survey the automatic relation extraction methods, especially the traditional machine learning on closed data set and open information environment such as Web, including supervised and semi-supervised methods. And then, we discuss the applications based on relation extraction such as event extraction and QA systems.","PeriodicalId":371106,"journal":{"name":"2016 International Conference on Identification, Information and Knowledge in the Internet of Things (IIKI)","volume":"87 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Automatic Relation Extraction from Text: A Survey\",\"authors\":\"Kun Li, Junsheng Zhang, Changqing Yao, Chongde Shi\",\"doi\":\"10.1109/IIKI.2016.58\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Relation extraction is an important task for understanding text. In the big data era, automatic relation extraction from unstructured texts is urgently needed for structured information organization and information analysis. In this paper, we survey the automatic relation extraction methods, especially the traditional machine learning on closed data set and open information environment such as Web, including supervised and semi-supervised methods. And then, we discuss the applications based on relation extraction such as event extraction and QA systems.\",\"PeriodicalId\":371106,\"journal\":{\"name\":\"2016 International Conference on Identification, Information and Knowledge in the Internet of Things (IIKI)\",\"volume\":\"87 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 International Conference on Identification, Information and Knowledge in the Internet of Things (IIKI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IIKI.2016.58\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 International Conference on Identification, Information and Knowledge in the Internet of Things (IIKI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IIKI.2016.58","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Relation extraction is an important task for understanding text. In the big data era, automatic relation extraction from unstructured texts is urgently needed for structured information organization and information analysis. In this paper, we survey the automatic relation extraction methods, especially the traditional machine learning on closed data set and open information environment such as Web, including supervised and semi-supervised methods. And then, we discuss the applications based on relation extraction such as event extraction and QA systems.