Overview
- 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 structure-based features, with no previous human-driven training, a feature unique to the authors’ approach
- Includes detailed experimental evaluations of different information extraction methods considering different domains and scenarios
- Includes supplementary material: sn.pub/extras
Part of the book series: SpringerBriefs in Computer Science (BRIEFSCOMPUTER)
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Table of contents (7 chapters)
Keywords
About this book
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 explicit delimiters between them. In comparison with other IETS methods, including ONDUX, JUDIE faces a task considerably harder that is, extracting information while simultaneously uncovering the underlying structure of the implicit records containing it. iForm applies the authors’ approach to the task of Web form filling. It aims at extracting segments from a data-rich text given as input and associating these segments with fields from a target Web form.
All of these methods were evaluated considering different experimental datasets, which are used to perform a large set of experiments in order to validate the presented approach and methods. These experiments indicate that the proposed approach yields high qualityresults when compared to state-of-the-art approaches and that it is able to properly support IETS methods in a number of real applications. The findings will prove valuable to practitioners in helping them to understand the current state-of-the-art in unsupervised information extraction techniques, as well as to graduate and undergraduate students of web data management.
Reviews
From the reviews:
“Cortez and da Silva discuss the problem of information extraction … . this book is most beneficial to researchers familiar with information extraction. … The book and the individual chapters are well organized and reasonably easy to follow, with just enough technical details to understand the functioning of the methods and systems described.” (Franz Kurfess, Computing Reviews, April, 2014)
Authors and Affiliations
Bibliographic Information
Book Title: Unsupervised Information Extraction by Text Segmentation
Authors: Eli Cortez, Altigran S. Silva
Series Title: SpringerBriefs in Computer Science
DOI: https://doi.org/10.1007/978-3-319-02597-1
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: The Author(s) 2013
Softcover ISBN: 978-3-319-02596-4Published: 11 November 2013
eBook ISBN: 978-3-319-02597-1Published: 23 October 2013
Series ISSN: 2191-5768
Series E-ISSN: 2191-5776
Edition Number: 1
Number of Pages: XV, 94
Number of Illustrations: 25 b/w illustrations
Topics: Database Management, Data Mining and Knowledge Discovery, Information Storage and Retrieval