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Unsupervised Information Extraction by Text Segmentation
53,49 €*
ISBN-13:
9783319025971
Veröffentl:
2013
Seiten:
94
Autor:
Eli Cortez
Serie:
SpringerBriefs in Computer Science
eBook Typ:
PDF
eBook Format:
EPUB
Kopierschutz:
1 - PDF Watermark
Sprache:
Englisch
Beschreibung:
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.
Foreword.- Preface.- Introduction.- Related Work.- Exploiting Pre-Existing Datasets to Support IETS.- ONDUX.- JUDIE.- iForm.- Conclusions and Future Work.