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Large scale text mining approaches for information retrieval and extraction

Authors
Bellot, PatriceBonnefoy, LudovicBouvier, VincentDuvert, FrédéricKim, Young Min
Issue Date
Jan-2014
Publisher
Springer
Citation
Studies in Computational Intelligence, v.514, pp.3 - 45
Indexed
SCOPUS
Journal Title
Studies in Computational Intelligence
Volume
514
Start Page
3
End Page
45
URI
https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/160817
DOI
10.1007/978-3-319-01866-9_1
ISSN
1860-949X
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.
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GRADUATE SCHOOL OF TECHNOLOGY & INNOVATION MANAGEMENT (DEPARTMENT OF TECHNOLOGY MANAGEMENT)
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