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Unsupervised Information Extraction by Text Segmentation

Unsupervised Information Extraction by Text Segmentation

Paperback

Series: Springerbriefs in Computer Science

DatabasesSystem Administration

ISBN10: 3319025961
ISBN13: 9783319025964
Publisher: Springer Nature
Published: Nov 11 2013
Pages: 94
Weight: 0.37
Height: 0.23 Width: 6.14 Depth: 9.21
Language: English

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.

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