Books > Computing & IT > Applications of computing > Databases
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An Incrementally Trainable Statistical Approach to Information Extraction - Based on Token Classification and Rich Context Model (Paperback)
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An Incrementally Trainable Statistical Approach to Information Extraction - Based on Token Classification and Rich Context Model (Paperback)
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Most of the information stored in digital form is hidden in natural
language texts. The purpose of Information Extraction (IE) is to
find desired pieces of information in unstructured or weakly
structured texts and store them in a form that is suitable for
automatic querying and processing. This book presents a innovative
approach to statistical information extraction. It introduces a new
algorithm which supports functionality not available in previous IE
systems, such as interactive incremental training to reduce the
human training effort. The system also utilizes new sources of
information, employing rich tree-based context representations to
combine document structure (HTML or XML markup) with linguistic and
semantic information. The resulting IE system is designed as a
generic framework for statistical information extraction. All core
components can be modified or exchanged independently of each
other. This book is of interest for professionals who have to deal
with large amounts of weakly structured information and seek ways
to automate this process, as well as for researchers and
practitioners active in the fields of text mining and text
classification.
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