eprintid: 3902
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/39/02
datestamp: 2023-11-09 15:52:10
lastmod: 2023-11-09 15:52:10
status_changed: 2023-11-09 15:47:53
type: article
metadata_visibility: show
creators_name: Zamin, N.
creators_name: Oxley, A.
creators_name: Bakar, Z.A.
title: Projecting named entity tags from a resource rich language to a resource poor language
ispublished: pub
note: cited By 3
abstract: 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.
date: 2013
publisher: Universiti Utara Malaysia Press
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84893004882&partnerID=40&md5=f8a6bb4af5b4d7f06f9e5e8666f91c47
full_text_status: none
publication: Journal of Information and Communication Technology
volume: 12
number: 1
pagerange: 121-146
refereed: TRUE
issn: 1675414X
citation:   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.  ISSN 1675414X