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Quantifying the Impact and Extent of Undocumented Biomedical Synonymy
Synonymous relationships among biomedical terms are extensively annotated within specialized terminologies, implying that synonymy is important for practical computational applications within this field. It remains unclear, however, whether text mining actually benefits from documented synonymy and...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4177665/ https://www.ncbi.nlm.nih.gov/pubmed/25255227 http://dx.doi.org/10.1371/journal.pcbi.1003799 |
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author | Blair, David R. Wang, Kanix Nestorov, Svetlozar Evans, James A. Rzhetsky, Andrey |
author_facet | Blair, David R. Wang, Kanix Nestorov, Svetlozar Evans, James A. Rzhetsky, Andrey |
author_sort | Blair, David R. |
collection | PubMed |
description | Synonymous relationships among biomedical terms are extensively annotated within specialized terminologies, implying that synonymy is important for practical computational applications within this field. It remains unclear, however, whether text mining actually benefits from documented synonymy and whether existing biomedical thesauri provide adequate coverage of these linguistic relationships. In this study, we examine the impact and extent of undocumented synonymy within a very large compendium of biomedical thesauri. First, we demonstrate that missing synonymy has a significant negative impact on named entity normalization, an important problem within the field of biomedical text mining. To estimate the amount synonymy currently missing from thesauri, we develop a probabilistic model for the construction of synonym terminologies that is capable of handling a wide range of potential biases, and we evaluate its performance using the broader domain of near-synonymy among general English words. Our model predicts that over 90% of these relationships are currently undocumented, a result that we support experimentally through “crowd-sourcing.” Finally, we apply our model to biomedical terminologies and predict that they are missing the vast majority (>90%) of the synonymous relationships they intend to document. Overall, our results expose the dramatic incompleteness of current biomedical thesauri and suggest the need for “next-generation,” high-coverage lexical terminologies. |
format | Online Article Text |
id | pubmed-4177665 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-41776652014-10-02 Quantifying the Impact and Extent of Undocumented Biomedical Synonymy Blair, David R. Wang, Kanix Nestorov, Svetlozar Evans, James A. Rzhetsky, Andrey PLoS Comput Biol Research Article Synonymous relationships among biomedical terms are extensively annotated within specialized terminologies, implying that synonymy is important for practical computational applications within this field. It remains unclear, however, whether text mining actually benefits from documented synonymy and whether existing biomedical thesauri provide adequate coverage of these linguistic relationships. In this study, we examine the impact and extent of undocumented synonymy within a very large compendium of biomedical thesauri. First, we demonstrate that missing synonymy has a significant negative impact on named entity normalization, an important problem within the field of biomedical text mining. To estimate the amount synonymy currently missing from thesauri, we develop a probabilistic model for the construction of synonym terminologies that is capable of handling a wide range of potential biases, and we evaluate its performance using the broader domain of near-synonymy among general English words. Our model predicts that over 90% of these relationships are currently undocumented, a result that we support experimentally through “crowd-sourcing.” Finally, we apply our model to biomedical terminologies and predict that they are missing the vast majority (>90%) of the synonymous relationships they intend to document. Overall, our results expose the dramatic incompleteness of current biomedical thesauri and suggest the need for “next-generation,” high-coverage lexical terminologies. Public Library of Science 2014-09-25 /pmc/articles/PMC4177665/ /pubmed/25255227 http://dx.doi.org/10.1371/journal.pcbi.1003799 Text en © 2014 Blair et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Blair, David R. Wang, Kanix Nestorov, Svetlozar Evans, James A. Rzhetsky, Andrey Quantifying the Impact and Extent of Undocumented Biomedical Synonymy |
title | Quantifying the Impact and Extent of Undocumented Biomedical Synonymy |
title_full | Quantifying the Impact and Extent of Undocumented Biomedical Synonymy |
title_fullStr | Quantifying the Impact and Extent of Undocumented Biomedical Synonymy |
title_full_unstemmed | Quantifying the Impact and Extent of Undocumented Biomedical Synonymy |
title_short | Quantifying the Impact and Extent of Undocumented Biomedical Synonymy |
title_sort | quantifying the impact and extent of undocumented biomedical synonymy |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4177665/ https://www.ncbi.nlm.nih.gov/pubmed/25255227 http://dx.doi.org/10.1371/journal.pcbi.1003799 |
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