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Projecting named entity tags from a resource rich language to a resource poor language

Named Entities (NE) are the prominent entities appearing in textual documents. Automatic classification of NE in a textual corpus is a vital process in Information Extraction and Information Retrieval research. Named Entity Recognition (NER) is the identification of words in text that correspond to...

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Main Authors:	Zamin, N., Oxley, A., Bakar, Z.A.
Format:	Article
Published:	2013
Online Access:	https://www.scopus.com/inward/record.uri?eid=2-s2.0-84893004882&partnerID=40&md5=f8a6bb4af5b4d7f06f9e5e8666f91c47 http://eprints.utp.edu.my/32546/
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spelling	my.utp.eprints.325462022-03-29T14:05:43Z Projecting named entity tags from a resource rich language to a resource poor language Zamin, N. Oxley, A. Bakar, Z.A. Named Entities (NE) are the prominent entities appearing in textual documents. Automatic classification of NE in a textual corpus is a vital process in Information Extraction and Information Retrieval research. Named Entity Recognition (NER) is the identification of words in text that correspond to a pre-defined taxonomy such as person, organization, location, date, time, etc. This article focuses on the person (PER), organization (ORG) and location (LOC) entities for a Malay journalistic corpus of terrorism. A projection algorithm, using the Dice Coefficient function and bigram scoring method with domain-specific rules, is suggested to map the NE information from the English corpus to the Malay corpus of terrorism. The English corpus is the translated version of the Malay corpus. Hence, these two corpora are treated as parallel corpora. The method computes the string similarity between the English words and the list of available lexemes in a pre-built lexicon that approximates the best NE mapping. The algorithm has been effectively evaluated using our own terrorism tagged corpus; it achieved satisfactory results in terms of precision, recall, and F-measure. An evaluation of the selected open source NER tool for English is also presented. 2013 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-84893004882&partnerID=40&md5=f8a6bb4af5b4d7f06f9e5e8666f91c47 Zamin, N. and Oxley, A. and Bakar, Z.A. (2013) Projecting named entity tags from a resource rich language to a resource poor language. Journal of Information and Communication Technology, 12 (1). pp. 121-146. http://eprints.utp.edu.my/32546/
institution	Universiti Teknologi Petronas
building	UTP Resource Centre
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Teknologi Petronas
content_source	UTP Institutional Repository
url_provider	http://eprints.utp.edu.my/
description	Named Entities (NE) are the prominent entities appearing in textual documents. Automatic classification of NE in a textual corpus is a vital process in Information Extraction and Information Retrieval research. Named Entity Recognition (NER) is the identification of words in text that correspond to a pre-defined taxonomy such as person, organization, location, date, time, etc. This article focuses on the person (PER), organization (ORG) and location (LOC) entities for a Malay journalistic corpus of terrorism. A projection algorithm, using the Dice Coefficient function and bigram scoring method with domain-specific rules, is suggested to map the NE information from the English corpus to the Malay corpus of terrorism. The English corpus is the translated version of the Malay corpus. Hence, these two corpora are treated as parallel corpora. The method computes the string similarity between the English words and the list of available lexemes in a pre-built lexicon that approximates the best NE mapping. The algorithm has been effectively evaluated using our own terrorism tagged corpus; it achieved satisfactory results in terms of precision, recall, and F-measure. An evaluation of the selected open source NER tool for English is also presented.
format	Article
author	Zamin, N. Oxley, A. Bakar, Z.A.
spellingShingle	Zamin, N. Oxley, A. Bakar, Z.A. Projecting named entity tags from a resource rich language to a resource poor language
author_facet	Zamin, N. Oxley, A. Bakar, Z.A.
author_sort	Zamin, N.
title	Projecting named entity tags from a resource rich language to a resource poor language
title_short	Projecting named entity tags from a resource rich language to a resource poor language
title_full	Projecting named entity tags from a resource rich language to a resource poor language
title_fullStr	Projecting named entity tags from a resource rich language to a resource poor language
title_full_unstemmed	Projecting named entity tags from a resource rich language to a resource poor language
title_sort	projecting named entity tags from a resource rich language to a resource poor language
publishDate	2013
url	https://www.scopus.com/inward/record.uri?eid=2-s2.0-84893004882&partnerID=40&md5=f8a6bb4af5b4d7f06f9e5e8666f91c47 http://eprints.utp.edu.my/32546/
_version_	1738657401981108224
score	13.211869

Projecting named entity tags from a resource rich language to a resource poor language

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