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Finding genomic ontology terms in text using evidence content

BACKGROUND: The development of text mining systems that annotate biological entities with their properties using scientific literature is an important recent research topic. These systems need first to recognize the biological entities and properties in the text, and then decide which pairs represen...

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Detalles Bibliográficos
Autores principales: Couto, Francisco M, Silva, Mário J, Coutinho, Pedro M
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2005
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1869014/
https://www.ncbi.nlm.nih.gov/pubmed/15960834
http://dx.doi.org/10.1186/1471-2105-6-S1-S21
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author Couto, Francisco M
Silva, Mário J
Coutinho, Pedro M
author_facet Couto, Francisco M
Silva, Mário J
Coutinho, Pedro M
author_sort Couto, Francisco M
collection PubMed
description BACKGROUND: The development of text mining systems that annotate biological entities with their properties using scientific literature is an important recent research topic. These systems need first to recognize the biological entities and properties in the text, and then decide which pairs represent valid annotations. METHODS: This document introduces a novel unsupervised method for recognizing biological properties in unstructured text, involving the evidence content of their names. RESULTS: This document shows the results obtained by the application of our method to BioCreative tasks 2.1 and 2.2, where it identified Gene Ontology annotations and their evidence in a set of articles. CONCLUSION: From the performance obtained in BioCreative, we concluded that an automatic annotation system can effectively use our method to identify biological properties in unstructured text.
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spelling pubmed-18690142007-05-18 Finding genomic ontology terms in text using evidence content Couto, Francisco M Silva, Mário J Coutinho, Pedro M BMC Bioinformatics Report BACKGROUND: The development of text mining systems that annotate biological entities with their properties using scientific literature is an important recent research topic. These systems need first to recognize the biological entities and properties in the text, and then decide which pairs represent valid annotations. METHODS: This document introduces a novel unsupervised method for recognizing biological properties in unstructured text, involving the evidence content of their names. RESULTS: This document shows the results obtained by the application of our method to BioCreative tasks 2.1 and 2.2, where it identified Gene Ontology annotations and their evidence in a set of articles. CONCLUSION: From the performance obtained in BioCreative, we concluded that an automatic annotation system can effectively use our method to identify biological properties in unstructured text. BioMed Central 2005-05-24 /pmc/articles/PMC1869014/ /pubmed/15960834 http://dx.doi.org/10.1186/1471-2105-6-S1-S21 Text en Copyright © 2005 Couto 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
Couto, Francisco M
Silva, Mário J
Coutinho, Pedro M
Finding genomic ontology terms in text using evidence content
title Finding genomic ontology terms in text using evidence content
title_full Finding genomic ontology terms in text using evidence content
title_fullStr Finding genomic ontology terms in text using evidence content
title_full_unstemmed Finding genomic ontology terms in text using evidence content
title_short Finding genomic ontology terms in text using evidence content
title_sort finding genomic ontology terms in text using evidence content
topic Report
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1869014/
https://www.ncbi.nlm.nih.gov/pubmed/15960834
http://dx.doi.org/10.1186/1471-2105-6-S1-S21
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