Detailed Information

Cited 0 time in webofscience Cited 0 time in scopus
Metadata Downloads

Large scale text mining approaches for information retrieval and extraction

Full metadata record
DC Field Value Language
dc.contributor.authorBellot, Patrice-
dc.contributor.authorBonnefoy, Ludovic-
dc.contributor.authorBouvier, Vincent-
dc.contributor.authorDuvert, Frédéric-
dc.contributor.authorKim, Young Min-
dc.date.accessioned2022-07-16T06:17:42Z-
dc.date.available2022-07-16T06:17:42Z-
dc.date.created2021-05-13-
dc.date.issued2014-01-
dc.identifier.issn1860-949X-
dc.identifier.urihttps://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/160817-
dc.description.abstractThe 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.-
dc.language영어-
dc.language.isoen-
dc.publisherSpringer-
dc.titleLarge scale text mining approaches for information retrieval and extraction-
dc.typeArticle-
dc.contributor.affiliatedAuthorKim, Young Min-
dc.identifier.doi10.1007/978-3-319-01866-9_1-
dc.identifier.scopusid2-s2.0-84958542535-
dc.identifier.bibliographicCitationStudies in Computational Intelligence, v.514, pp.3 - 45-
dc.relation.isPartOfStudies in Computational Intelligence-
dc.citation.titleStudies in Computational Intelligence-
dc.citation.volume514-
dc.citation.startPage3-
dc.citation.endPage45-
dc.type.rimsART-
dc.type.docType정기 학술지(기타)-
dc.description.journalClass1-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscopus-
dc.identifier.urlhttps://link.springer.com/chapter/10.1007/978-3-319-01866-9_1-
Files in This Item
Go to Link
Appears in
Collections
서울 기술경영전문대학원 > 서울 기술경영학과 > 1. Journal Articles

qrcode

Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.

Related Researcher

Researcher Kim, Young min photo

Kim, Young min
GRADUATE SCHOOL OF TECHNOLOGY & INNOVATION MANAGEMENT (DEPARTMENT OF TECHNOLOGY MANAGEMENT)
Read more

Altmetrics

Total Views & Downloads

BROWSE