Extended distributed prototypical for biomedical named entity recognition

Biomedical Named Entity Recognition (Bio-NER) is an essential step of biomedical information extraction and biomedical text mining. Although, a lot of researches have been made in the design of rule-based and supervised tools for general NER, Bio-NER still remains a challenge and an area of active r...

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Main Authors:	Maan Tareq Abd, Masnizah Mohd
Format:	Article
Language:	English
Published:	Penerbit Universiti Kebangsaan Malaysia 2017
Online Access:	http://journalarticle.ukm.my/11849/ http://journalarticle.ukm.my/11849/ http://journalarticle.ukm.my/11849/1/18684-64021-1-PB.pdf

id	ukm-11849
recordtype	eprints
spelling	ukm-118492018-07-10T00:19:23Z http://journalarticle.ukm.my/11849/ Extended distributed prototypical for biomedical named entity recognition Maan Tareq Abd, Masnizah Mohd, Biomedical Named Entity Recognition (Bio-NER) is an essential step of biomedical information extraction and biomedical text mining. Although, a lot of researches have been made in the design of rule-based and supervised tools for general NER, Bio-NER still remains a challenge and an area of active research, as still there is huge difference in F-score of 10 points between general newswire NER and Bio-NER. The complex structures of the biomedical entities pose a huge challenge for their recognition. To handle this, this paper explores different effective word representations with Support Vector Machine (SVM) to deal with the complex structures of biomedical named entities. First, this paper identifies and evaluates a set of morphological and contextual features with SVM learning method for Bio-NER. This paper also presents an extended distributed representation word embedding technique (EDRWE) for Bio-NER. These models are evaluated on widely used standard Bio-NER dataset namely GENIA corpus. Experimental results show that EDRWE technique improves the overall performance of the Bio-NER and outperforms all other representation methods. Results analysis shows that the new EDRWE is satisfactory and effective for Bio-NER especially when only a small-sized data set is available. Penerbit Universiti Kebangsaan Malaysia 2017-12 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/11849/1/18684-64021-1-PB.pdf Maan Tareq Abd, and Masnizah Mohd, (2017) Extended distributed prototypical for biomedical named entity recognition. Asia-Pacific Journal of Information Technology and Multimedia, 6 (2). pp. 1-11. ISSN 2289-2192 http://ejournals.ukm.my/apjitm/issue/view/1050
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Kebangasaan Malaysia
building	UKM Institutional Repository
collection	Online Access
language	English
description	Biomedical Named Entity Recognition (Bio-NER) is an essential step of biomedical information extraction and biomedical text mining. Although, a lot of researches have been made in the design of rule-based and supervised tools for general NER, Bio-NER still remains a challenge and an area of active research, as still there is huge difference in F-score of 10 points between general newswire NER and Bio-NER. The complex structures of the biomedical entities pose a huge challenge for their recognition. To handle this, this paper explores different effective word representations with Support Vector Machine (SVM) to deal with the complex structures of biomedical named entities. First, this paper identifies and evaluates a set of morphological and contextual features with SVM learning method for Bio-NER. This paper also presents an extended distributed representation word embedding technique (EDRWE) for Bio-NER. These models are evaluated on widely used standard Bio-NER dataset namely GENIA corpus. Experimental results show that EDRWE technique improves the overall performance of the Bio-NER and outperforms all other representation methods. Results analysis shows that the new EDRWE is satisfactory and effective for Bio-NER especially when only a small-sized data set is available.
format	Article
author	Maan Tareq Abd, Masnizah Mohd,
spellingShingle	Maan Tareq Abd, Masnizah Mohd, Extended distributed prototypical for biomedical named entity recognition
author_facet	Maan Tareq Abd, Masnizah Mohd,
author_sort	Maan Tareq Abd,
title	Extended distributed prototypical for biomedical named entity recognition
title_short	Extended distributed prototypical for biomedical named entity recognition
title_full	Extended distributed prototypical for biomedical named entity recognition
title_fullStr	Extended distributed prototypical for biomedical named entity recognition
title_full_unstemmed	Extended distributed prototypical for biomedical named entity recognition
title_sort	extended distributed prototypical for biomedical named entity recognition
publisher	Penerbit Universiti Kebangsaan Malaysia
publishDate	2017
url	http://journalarticle.ukm.my/11849/ http://journalarticle.ukm.my/11849/ http://journalarticle.ukm.my/11849/1/18684-64021-1-PB.pdf
first_indexed	2023-09-18T20:01:17Z
last_indexed	2023-09-18T20:01:17Z
_version_	1777406865420845056

Extended distributed prototypical for biomedical named entity recognition

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