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Author Turmo, Jordi ♦ Ageno, Alicia ♦ Catal, Neus
Source ACM Digital Library
Content type Text
Publisher Association for Computing Machinery (ACM)
File Format PDF
Copyright Year ©2006
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Information extraction ♦ Machine learning
Abstract The growing availability of online textual sources and the potential number of applications of knowledge acquisition from textual data has lead to an increase in Information Extraction (IE) research. Some examples of these applications are the generation of data bases from documents, as well as the acquisition of knowledge useful for emerging technologies like question answering, information integration, and others related to text mining. However, one of the main drawbacks of the application of IE refers to its intrinsic domain dependence. For the sake of reducing the high cost of manually adapting IE applications to new domains, experiments with different Machine Learning (ML) techniques have been carried out by the research community. This survey describes and compares the main approaches to IE and the different ML techniques used to achieve Adaptive IE technology.
ISSN 03600300
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2006-07-01
Publisher Place New York
e-ISSN 15577341
Journal ACM Computing Surveys (CSUR)
Volume Number 38
Issue Number 2


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Source: ACM Digital Library