relation: https://khub.utp.edu.my/scholars/1993/
title: Building a corpus-derived gazetteer for named entity recognition
creator: Zamin, N.
creator: Oxley, A.
description: Gazetteers, or entity dictionaries, are an important element for Named Entity Recognition. Named Entity Recognition is an essential component of Information Extraction. Gazetteers work as specialized dictionaries to support initial tagging. They provide quick entity identification thus creating richer document representation. However, the compilation of such gazetteers is sometimes mentioned as a stumbling block in Named Entity Recognition. Machine learning, both rule-based and look-up based approaches, are often used to perform this process. In this paper, a gazetteer developed from MUC-3 annotated data for the 'person named' entity type is presented. The process used has a small computational cost. We combine rule-based grammars and a simple filtering technique for automatically inducing the gazetteer. We conclude with experiments to compare the content of the gazetteer with the manually crafted one. © 2011 Springer-Verlag.
date: 2011
type: Article
type: PeerReviewed
identifier:   Zamin, N. and Oxley, A.  (2011) Building a corpus-derived gazetteer for named entity recognition.  Communications in Computer and Information Science, 180 CC (PART 2).  pp. 73-80.  ISSN 18650929     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-79960375105&doi=10.1007%2f978-3-642-22191-0_6&partnerID=40&md5=91d7dd8f8824c47f81a3a320f495ffa0
relation: 10.1007/978-3-642-22191-0₆
identifier: 10.1007/978-3-642-22191-0₆