Publication Type

Book Chapter

Publication Date

2012

Abstract

Information extraction is the task of finding structured information from unstructured or semi-structured text. It is an important task in text mining and has been extensively studied in various research communities including natural language processing, information retrieval and Web mining. It has a wide range of applications in domains such as biomedical literature mining and business intelligence. Two fundamental tasks of information extraction are named entity recognition and relation extraction. The former refers to finding names of entities such as people, organizations and locations. The latter refers to finding the semantic relations such as FounderOf and HeadquarteredIn between entities. In this chapter we provide a survey of the major work on named entity recognition and relation extraction in the past few decades, with a focus on work from the natural language processing community.

Keywords

Information extraction, named entity recognition, relation extraction

Discipline

Databases and Information Systems

Research Areas

Data Management and Analytics

Publication

Mining Text Data

Editor

Aggarwal, Charu C.; Zhai, Cheng-Xiang

First Page

11

Last Page

41

ISBN

9781461432227

Identifier

10.1007/978-1-4614-3223-4_2

Publisher

Springer Verlag

City or Country

New York

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://dx.doi.org/10.1007/978-1-4614-3223-4_2

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