Information Extraction

Claire Nédellec (), Adeline Nazarenko () and Robert Bossy
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Claire Nédellec: INRA
Adeline Nazarenko: Université Paris-Nord
Robert Bossy: INRA

A chapter in Handbook on Ontologies, 2009, pp 663-685 from Springer

Abstract: Summary Information Extraction (IE) addresses the intelligent access to document contents by automatically extracting information relevant to a given task. This chapter focuses on how ontologies can be exploited to interpret the textual document content for IE purposes. It makes a state of the art of IE systems from the point of view of IE as a knowledge-based NLP process. It reviews the different steps of NLP necessary for IE tasks: named entity recognition, term analysis, semantic typing and identification specific relations. It stresses on the importance of ontological knowledge for performing each step and presents corpus-based methods for the acquisition of the required knowlege. This chapter shows that IE is an ontology-based activity and argues that future effort in IE should focus on formalizing and reinforcing the relation between the text extraction and the ontology model. The discussion gives authors’ insights on the integration of ontological knowledge in IE systems from a formal and pragmatic point of view. Examples in this chapter are taken from IE tasks for biology since this domain attracts a large community of IE specialists and provides a large number of ontological resources.

Keywords: Information Extraction; Training Corpus; Extraction Rule; Semantic Unit; Lexical Knowledge (search for similar items in EconPapers)
Date: 2009
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DOI: 10.1007/978-3-540-92673-3_30

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