Projecting Named Entity Tags from a Resource Rich Language to a Resource Poor Language

Authors

  • Norshuhani Zamin Faculty of Science and Information Technology, Universiti Teknologi PETRONAS Bandar Seri Iskandar, 31750 Tronoh, Perak, Malaysia
  • Alan Oxley Faculty of Science and Information Technology, Universiti Teknologi PETRONAS Bandar Seri Iskandar, 31750 Tronoh, Perak, Malaysia
  • Zainab Abu Bakar Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA 40450 Shah Alam, Selangor, Malaysia

DOI:

https://doi.org/10.32890/jict2013.12.7

Keywords:

Named entity recognition, information projection, bitext alignment, resource poor language, unsupervised learning, Malay terrorism corpus

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.

 

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Published

23-04-2013

Issue

Vol. 12 (2013): Journal of Information and Communication Technology (JICT) Vol. 12, 2013

Section

Articles

How to Cite

Zamin, N., Oxley, A., & Abu Bakar, Z. (2013). Projecting Named Entity Tags from a Resource Rich Language to a Resource Poor Language. Journal of Information and Communication Technology, 12, 121-146. https://doi.org/10.32890/jict2013.12.7

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Identifiers DOI 10.32890/jict2013.12.7