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Titlebook: Unsupervised Information Extraction by Text Segmentation; Eli Cortez,Altigran S. Silva Book 2013 The Author(s) 2013 Databases.Information

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發(fā)表于 2025-3-21 16:19:30 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Unsupervised Information Extraction by Text Segmentation
編輯Eli Cortez,Altigran S. Silva
視頻videohttp://file.papertrans.cn/943/942525/942525.mp4
概述Presents and evaluates a new unsupervised approach for the problem of Information Extraction by Text Segmentation (IETS).Describes how to automatically use content-based features to directly learn str
叢書名稱SpringerBriefs in Computer Science
圖書封面Titlebook: Unsupervised Information Extraction by Text Segmentation;  Eli Cortez,Altigran S. Silva Book 2013 The Author(s) 2013 Databases.Information
描述.A new unsupervised approach to the problem of Information Extraction by Text Segmentation (IETS) is proposed, implemented and evaluated herein. The authors’ approach relies on information available on pre-existing data to learn how to associate segments in the input string with attributes of a given domain relying on a very effective set of content-based features. The effectiveness of the content-based features is also exploited to directly learn from test data structure-based features, with no previous human-driven training, a feature unique to the presented approach. Based on the approach, a number of results are produced to address the IETS problem in an unsupervised fashion. In particular, the authors develop, implement and evaluate distinct IETS methods, namely .ONDUX., .JUDIE. and .iForm...ONDUX. (On Demand Unsupervised Information Extraction) is an unsupervised probabilistic approach for IETS that relies on content-based features to bootstrap the learning of structure-based features. .JUDIE. (Joint Unsupervised Structure Discovery and Information Extraction) aims at automatically extracting several semi-structured data records in the form of continuous text and having no ex
出版日期Book 2013
關(guān)鍵詞Databases; Information Extraction; Knowledge Bases; Markov Models; Structured Data; Text Segmentation; Tex
版次1
doihttps://doi.org/10.1007/978-3-319-02597-1
isbn_softcover978-3-319-02596-4
isbn_ebook978-3-319-02597-1Series ISSN 2191-5768 Series E-ISSN 2191-5776
issn_series 2191-5768
copyrightThe Author(s) 2013
The information of publication is updating

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發(fā)表于 2025-3-21 23:00:40 | 只看該作者
,,ed approach to deal with the Information Extraction by Text Segmentation problem. . was first presented in Toda et al. (., .). In the following is described the scenario where . is applied, and the method in detail. A set of experiments is also reported that shows that . is effective and works well in different scenarios.
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https://doi.org/10.1007/978-3-319-02597-1Databases; Information Extraction; Knowledge Bases; Markov Models; Structured Data; Text Segmentation; Tex
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Conclusions and Future Work,This chapter presents the conclusions and discuss directions for future work based on the unsupervised approach presented here.
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