{"title":"流新闻文档分析的动态图模型","authors":"E. Hohman, D. Marchette","doi":"10.1109/CIDM.2007.368911","DOIUrl":null,"url":null,"abstract":"In this paper we consider the problem of analyzing streaming documents, in particular streaming news stories. The system is designed to extract statistics from the document, incorporate these into a graph-based model, and discard the document to reduce storage requirements. The model is defined in terms of a changing lexicon and sub-lexicons at each node in the graph, with the nodes of the graph representing topics. An approximation to the TFIDF term weighting is introduced. We illustrate the methodology on a dataset of news articles, and discuss the dynamic nature of the model","PeriodicalId":423707,"journal":{"name":"2007 IEEE Symposium on Computational Intelligence and Data Mining","volume":"2015 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"A Dynamic Graph Model for Analyzing Streaming News Documents\",\"authors\":\"E. Hohman, D. Marchette\",\"doi\":\"10.1109/CIDM.2007.368911\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper we consider the problem of analyzing streaming documents, in particular streaming news stories. The system is designed to extract statistics from the document, incorporate these into a graph-based model, and discard the document to reduce storage requirements. The model is defined in terms of a changing lexicon and sub-lexicons at each node in the graph, with the nodes of the graph representing topics. An approximation to the TFIDF term weighting is introduced. We illustrate the methodology on a dataset of news articles, and discuss the dynamic nature of the model\",\"PeriodicalId\":423707,\"journal\":{\"name\":\"2007 IEEE Symposium on Computational Intelligence and Data Mining\",\"volume\":\"2015 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2007-06-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2007 IEEE Symposium on Computational Intelligence and Data Mining\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CIDM.2007.368911\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2007 IEEE Symposium on Computational Intelligence and Data Mining","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CIDM.2007.368911","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Dynamic Graph Model for Analyzing Streaming News Documents
In this paper we consider the problem of analyzing streaming documents, in particular streaming news stories. The system is designed to extract statistics from the document, incorporate these into a graph-based model, and discard the document to reduce storage requirements. The model is defined in terms of a changing lexicon and sub-lexicons at each node in the graph, with the nodes of the graph representing topics. An approximation to the TFIDF term weighting is introduced. We illustrate the methodology on a dataset of news articles, and discuss the dynamic nature of the model
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