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 quality results 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."
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