| 书目名称 | Unsupervised Information Extraction by Text Segmentation |
| 编辑 | Eli Cortez,Altigran S. Silva |
| 视频video | |
| 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 |
| 丛书名称 | SpringerBriefs in Computer Science |
| 图书封面 |  |
| 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 |
| 关键词 | Databases; Information Extraction; Knowledge Bases; Markov Models; Structured Data; Text Segmentation; Tex |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-319-02597-1 |
| isbn_softcover | 978-3-319-02596-4 |
| isbn_ebook | 978-3-319-02597-1Series ISSN 2191-5768 Series E-ISSN 2191-5776 |
| issn_series | 2191-5768 |
| copyright | The Author(s) 2013 |
发表于 2025-3-21 16:19:30
