Staff View: A hybrid approach to semi-supervised named entity recognition in health, safety and environment reports

A hybrid approach to semi-supervised named entity recognition in health, safety and environment reports

In the last few years, text mining have become the area of interests in Natural Language Processing (NLP). They share a similar idea i.e. to extract important facts from unstructured text which later help to populate database entries. Name Entity Recognition (NER) is one of the main task needed to d...

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Main Authors:	Sari, Y., Hassan, M.F., Zamin, N.
Format:	Conference or Workshop Item
Published:	2009
Subjects:	QA75 Electronic computers. Computer science QA76 Computer software
Online Access:	http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=05189853 http://eprints.utp.edu.my/1777/
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id	my.utp.eprints.1777
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spelling	my.utp.eprints.17772010-05-09T17:16:09Z A hybrid approach to semi-supervised named entity recognition in health, safety and environment reports Sari, Y. Hassan, M.F. Zamin, N. QA75 Electronic computers. Computer science QA76 Computer software In the last few years, text mining have become the area of interests in Natural Language Processing (NLP). They share a similar idea i.e. to extract important facts from unstructured text which later help to populate database entries. Name Entity Recognition (NER) is one of the main task needed to develop text mining systems in which it is used to identify and classify entities in the text into predefined categories such as the names of persons, organizations, locations, dates, times, quantities, monetary values, percentages, etc. This paper focuses on studying the optimum solution to perform NER. To achieve our target, Health Safety and Environment (HSE) reports available from the Universiti Teknologi PETRONAS (UTP) are chosen as the case study. The UTP's HSE reports are the investigation reports which contain the information on incidents and accidents occurred during the daily operations. Many algorithms have been reported for NER ranging from simple statistical methods to advanced Natural language Processing (NLP) methods. This paper describes the possibility to apply Link Grammar (LG) and Basilisk Algorithm in NER. 2009-04 Conference or Workshop Item PeerReviewed http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=05189853 Sari, Y. and Hassan, M.F. and Zamin, N. (2009) A hybrid approach to semi-supervised named entity recognition in health, safety and environment reports. In: International Conference on Future Computer and Communication (ICFCC 2009), 3-5 April 2009, Kuala Lumpur. http://eprints.utp.edu.my/1777/
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/
topic	QA75 Electronic computers. Computer science QA76 Computer software
spellingShingle	QA75 Electronic computers. Computer science QA76 Computer software Sari, Y. Hassan, M.F. Zamin, N. A hybrid approach to semi-supervised named entity recognition in health, safety and environment reports
description	In the last few years, text mining have become the area of interests in Natural Language Processing (NLP). They share a similar idea i.e. to extract important facts from unstructured text which later help to populate database entries. Name Entity Recognition (NER) is one of the main task needed to develop text mining systems in which it is used to identify and classify entities in the text into predefined categories such as the names of persons, organizations, locations, dates, times, quantities, monetary values, percentages, etc. This paper focuses on studying the optimum solution to perform NER. To achieve our target, Health Safety and Environment (HSE) reports available from the Universiti Teknologi PETRONAS (UTP) are chosen as the case study. The UTP's HSE reports are the investigation reports which contain the information on incidents and accidents occurred during the daily operations. Many algorithms have been reported for NER ranging from simple statistical methods to advanced Natural language Processing (NLP) methods. This paper describes the possibility to apply Link Grammar (LG) and Basilisk Algorithm in NER.
format	Conference or Workshop Item
author	Sari, Y. Hassan, M.F. Zamin, N.
author_facet	Sari, Y. Hassan, M.F. Zamin, N.
author_sort	Sari, Y.
title	A hybrid approach to semi-supervised named entity recognition in health, safety and environment reports
title_short	A hybrid approach to semi-supervised named entity recognition in health, safety and environment reports
title_full	A hybrid approach to semi-supervised named entity recognition in health, safety and environment reports
title_fullStr	A hybrid approach to semi-supervised named entity recognition in health, safety and environment reports
title_full_unstemmed	A hybrid approach to semi-supervised named entity recognition in health, safety and environment reports
title_sort	hybrid approach to semi-supervised named entity recognition in health, safety and environment reports
publishDate	2009
url	http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=05189853 http://eprints.utp.edu.my/1777/
_version_	1738655152632496128
score	13.211869

A hybrid approach to semi-supervised named entity recognition in health, safety and environment reports

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