{"title":"A Comprehensive Evaluation of a Novel Approach to Probabilistic Information Extraction from Large Unstructured Datasets","authors":"M. Trovati","doi":"10.1109/INCoS.2015.36","DOIUrl":null,"url":null,"abstract":"In this paper, we discuss the evaluation of the probabilistic extraction as introduced in [1], by considering three different datasets introduced in [1] - [3]. the results show the potential of the approach, as well as its reliability and efficiency when analyzing datasets with different properties and structures. This is part of ongoing research aiming to provide a tool to extract, assess and visualize intelligence extracted from large unstructured datasets.","PeriodicalId":345650,"journal":{"name":"2015 International Conference on Intelligent Networking and Collaborative Systems","volume":"22 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 International Conference on Intelligent Networking and Collaborative Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/INCoS.2015.36","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
In this paper, we discuss the evaluation of the probabilistic extraction as introduced in [1], by considering three different datasets introduced in [1] - [3]. the results show the potential of the approach, as well as its reliability and efficiency when analyzing datasets with different properties and structures. This is part of ongoing research aiming to provide a tool to extract, assess and visualize intelligence extracted from large unstructured datasets.
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