{"title":"阅读字里行间:消除推文地理位置歧义的机器学习方法","authors":"Sunshin Lee, M. Farag, Tarek Kanan, E. Fox","doi":"10.1145/2756406.2756971","DOIUrl":null,"url":null,"abstract":"This paper describes a Machine Learning (ML) approach for extracting named entities and disambiguating the location of tweets based on those named entities and related content. We conducted experiments with tweets (e.g., about potholes), and found significant improvement in disambiguating tweet locations using a ML algorithm along with the Stanford NER. Adding state information predicted by our classifiers increases the possibility to find the state-level geo-location unambiguously by up to 80%.","PeriodicalId":256118,"journal":{"name":"Proceedings of the 15th ACM/IEEE-CS Joint Conference on Digital Libraries","volume":"153 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"19","resultStr":"{\"title\":\"Read between the lines: A Machine Learning Approach for Disambiguating the Geo-location of Tweets\",\"authors\":\"Sunshin Lee, M. Farag, Tarek Kanan, E. Fox\",\"doi\":\"10.1145/2756406.2756971\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper describes a Machine Learning (ML) approach for extracting named entities and disambiguating the location of tweets based on those named entities and related content. We conducted experiments with tweets (e.g., about potholes), and found significant improvement in disambiguating tweet locations using a ML algorithm along with the Stanford NER. Adding state information predicted by our classifiers increases the possibility to find the state-level geo-location unambiguously by up to 80%.\",\"PeriodicalId\":256118,\"journal\":{\"name\":\"Proceedings of the 15th ACM/IEEE-CS Joint Conference on Digital Libraries\",\"volume\":\"153 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-06-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"19\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 15th ACM/IEEE-CS Joint Conference on Digital Libraries\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2756406.2756971\",\"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 15th ACM/IEEE-CS Joint Conference on Digital Libraries","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2756406.2756971","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Read between the lines: A Machine Learning Approach for Disambiguating the Geo-location of Tweets
This paper describes a Machine Learning (ML) approach for extracting named entities and disambiguating the location of tweets based on those named entities and related content. We conducted experiments with tweets (e.g., about potholes), and found significant improvement in disambiguating tweet locations using a ML algorithm along with the Stanford NER. Adding state information predicted by our classifiers increases the possibility to find the state-level geo-location unambiguously by up to 80%.