Colin Lockard, Prashant Shiralkar, Xin Dong, Hannaneh Hajishirzi
{"title":"网络规模的知识收集","authors":"Colin Lockard, Prashant Shiralkar, Xin Dong, Hannaneh Hajishirzi","doi":"10.1145/3336191.3371878","DOIUrl":null,"url":null,"abstract":"How do we surface the large amount of information present in HTML documents on the Web, from news articles to scientific papers to Rotten Tomatoes pages to tables of sports scores? Such information can enable a variety of applications including knowledge base construction, question answering, recommendation, and more. In this tutorial, we present approaches for Information Extraction (IE) from Web data that can be differentiated along two key dimensions: 1) the diversity in data modality that is leveraged, e.g. text, visual, XML/HTML, and 2) the thrust to develop scalable approaches with zero to limited human supervision. We cover the key ideas and intuition behind existing approaches to emphasize their applicability and potential in various settings.","PeriodicalId":319008,"journal":{"name":"Proceedings of the 13th International Conference on Web Search and Data Mining","volume":"124 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Web-scale Knowledge Collection\",\"authors\":\"Colin Lockard, Prashant Shiralkar, Xin Dong, Hannaneh Hajishirzi\",\"doi\":\"10.1145/3336191.3371878\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"How do we surface the large amount of information present in HTML documents on the Web, from news articles to scientific papers to Rotten Tomatoes pages to tables of sports scores? Such information can enable a variety of applications including knowledge base construction, question answering, recommendation, and more. In this tutorial, we present approaches for Information Extraction (IE) from Web data that can be differentiated along two key dimensions: 1) the diversity in data modality that is leveraged, e.g. text, visual, XML/HTML, and 2) the thrust to develop scalable approaches with zero to limited human supervision. We cover the key ideas and intuition behind existing approaches to emphasize their applicability and potential in various settings.\",\"PeriodicalId\":319008,\"journal\":{\"name\":\"Proceedings of the 13th International Conference on Web Search and Data Mining\",\"volume\":\"124 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-01-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 13th International Conference on Web Search and Data Mining\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3336191.3371878\",\"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 13th International Conference on Web Search and Data Mining","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3336191.3371878","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
How do we surface the large amount of information present in HTML documents on the Web, from news articles to scientific papers to Rotten Tomatoes pages to tables of sports scores? Such information can enable a variety of applications including knowledge base construction, question answering, recommendation, and more. In this tutorial, we present approaches for Information Extraction (IE) from Web data that can be differentiated along two key dimensions: 1) the diversity in data modality that is leveraged, e.g. text, visual, XML/HTML, and 2) the thrust to develop scalable approaches with zero to limited human supervision. We cover the key ideas and intuition behind existing approaches to emphasize their applicability and potential in various settings.
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