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GENETAG: a tagged corpus for gene/protein named entity recognition

BACKGROUND: Named entity recognition (NER) is an important first step for text mining the biomedical literature. Evaluating the performance of biomedical NER systems is impossible without a standardized test corpus. The annotation of such a corpus for gene/protein name NER is a difficult process due...

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Autores principales: Tanabe, Lorraine, Xie, Natalie, Thom, Lynne H, Matten, Wayne, Wilbur, W John
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2005
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1869017/
https://www.ncbi.nlm.nih.gov/pubmed/15960837
http://dx.doi.org/10.1186/1471-2105-6-S1-S3
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author Tanabe, Lorraine
Xie, Natalie
Thom, Lynne H
Matten, Wayne
Wilbur, W John
author_facet Tanabe, Lorraine
Xie, Natalie
Thom, Lynne H
Matten, Wayne
Wilbur, W John
author_sort Tanabe, Lorraine
collection PubMed
description BACKGROUND: Named entity recognition (NER) is an important first step for text mining the biomedical literature. Evaluating the performance of biomedical NER systems is impossible without a standardized test corpus. The annotation of such a corpus for gene/protein name NER is a difficult process due to the complexity of gene/protein names. We describe the construction and annotation of GENETAG, a corpus of 20K MEDLINE(® )sentences for gene/protein NER. 15K GENETAG sentences were used for the BioCreAtIvE Task 1A Competition. RESULTS: To ensure heterogeneity of the corpus, MEDLINE sentences were first scored for term similarity to documents with known gene names, and 10K high- and 10K low-scoring sentences were chosen at random. The original 20K sentences were run through a gene/protein name tagger, and the results were modified manually to reflect a wide definition of gene/protein names subject to a specificity constraint, a rule that required the tagged entities to refer to specific entities. Each sentence in GENETAG was annotated with acceptable alternatives to the gene/protein names it contained, allowing for partial matching with semantic constraints. Semantic constraints are rules requiring the tagged entity to contain its true meaning in the sentence context. Application of these constraints results in a more meaningful measure of the performance of an NER system than unrestricted partial matching. CONCLUSION: The annotation of GENETAG required intricate manual judgments by annotators which hindered tagging consistency. The data were pre-segmented into words, to provide indices supporting comparison of system responses to the "gold standard". However, character-based indices would have been more robust than word-based indices. GENETAG Train, Test and Round1 data and ancillary programs are freely available at . A newer version of GENETAG-05, will be released later this year.
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spelling pubmed-18690172007-05-18 GENETAG: a tagged corpus for gene/protein named entity recognition Tanabe, Lorraine Xie, Natalie Thom, Lynne H Matten, Wayne Wilbur, W John BMC Bioinformatics Report BACKGROUND: Named entity recognition (NER) is an important first step for text mining the biomedical literature. Evaluating the performance of biomedical NER systems is impossible without a standardized test corpus. The annotation of such a corpus for gene/protein name NER is a difficult process due to the complexity of gene/protein names. We describe the construction and annotation of GENETAG, a corpus of 20K MEDLINE(® )sentences for gene/protein NER. 15K GENETAG sentences were used for the BioCreAtIvE Task 1A Competition. RESULTS: To ensure heterogeneity of the corpus, MEDLINE sentences were first scored for term similarity to documents with known gene names, and 10K high- and 10K low-scoring sentences were chosen at random. The original 20K sentences were run through a gene/protein name tagger, and the results were modified manually to reflect a wide definition of gene/protein names subject to a specificity constraint, a rule that required the tagged entities to refer to specific entities. Each sentence in GENETAG was annotated with acceptable alternatives to the gene/protein names it contained, allowing for partial matching with semantic constraints. Semantic constraints are rules requiring the tagged entity to contain its true meaning in the sentence context. Application of these constraints results in a more meaningful measure of the performance of an NER system than unrestricted partial matching. CONCLUSION: The annotation of GENETAG required intricate manual judgments by annotators which hindered tagging consistency. The data were pre-segmented into words, to provide indices supporting comparison of system responses to the "gold standard". However, character-based indices would have been more robust than word-based indices. GENETAG Train, Test and Round1 data and ancillary programs are freely available at . A newer version of GENETAG-05, will be released later this year. BioMed Central 2005-05-24 /pmc/articles/PMC1869017/ /pubmed/15960837 http://dx.doi.org/10.1186/1471-2105-6-S1-S3 Text en Copyright © 2005 Tanabe et al; licensee BioMed Central Ltd http://creativecommons.org/licenses/by/2.0 This is an open access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Report
Tanabe, Lorraine
Xie, Natalie
Thom, Lynne H
Matten, Wayne
Wilbur, W John
GENETAG: a tagged corpus for gene/protein named entity recognition
title GENETAG: a tagged corpus for gene/protein named entity recognition
title_full GENETAG: a tagged corpus for gene/protein named entity recognition
title_fullStr GENETAG: a tagged corpus for gene/protein named entity recognition
title_full_unstemmed GENETAG: a tagged corpus for gene/protein named entity recognition
title_short GENETAG: a tagged corpus for gene/protein named entity recognition
title_sort genetag: a tagged corpus for gene/protein named entity recognition
topic Report
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1869017/
https://www.ncbi.nlm.nih.gov/pubmed/15960837
http://dx.doi.org/10.1186/1471-2105-6-S1-S3
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