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Large scale text mining approaches for information retrieval and extraction

Title
Large scale text mining approaches for information retrieval and extraction
Author
김영민
Keywords
Entropy; Marketing; Suffix; Karen
Issue Date
2014-01
Publisher
Springer Verlag
Citation
Studies in Computational Intelligence, Vol. 514, 2014, Pages 3-45
Abstract
The issues for Natural Language Processing and Information Retrieval have been studied for long time but the recent availability of very large resources (Web pages, digital documents.) and the development of statistical machine learning methods exploiting annotated texts (manual encoding by crowdsourcing is a new major way) have transformed these fields. This allows not limiting these approaches to highly specialized domains and reducing the cost of their implementation. For this chapter, our aim is to present some popular text-mining statistical approaches for information retrieval and information extraction and to discuss the practical limits of actual systems that introduce challenges for future. ⓒ 2014 Springer International Publishing Switzerland.
URI
http://link.springer.com/chapter/10.1007%2F978-3-319-01866-9_1http://hdl.handle.net/20.500.11754/47494
ISBN
978-3-319-01865-2; 978-3-319-01866-9
ISSN
1860-949X
DOI
10.1007/978-3-319-01866-9_1
Appears in Collections:
GRADUATE SCHOOL OF TECHNOLOGY & INNOVATION MANAGEMENT[S](기술경영전문대학원) > ETC
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