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|Título :||A sentence sliding window approach to extract protein annotations from biomedical articles|
|Autor :||Krallinger, Martin; Padron, Maria; Valencia, Alfonso|
|Fecha de publicación :||24-may-2005|
|Citación :||BMC Bioinformatics 2005, 6(Suppl 1):S19|
|Resumen:||[Background] Within the emerging field of text mining and statistical natural language processing (NLP) applied to biomedical articles, a broad variety of techniques have been developed during the past years. Nevertheless, there is still a great ned of comparative assessment of the performance of the proposed methods and the development of common evaluation criteria. This issue was addressed by the Critical Assessment of Text Mining Methods in Molecular Biology (BioCreative) contest. The aim of this contest was to assess the performance of text mining systems applied to biomedical texts including tools which recognize named entities such as genes and proteins, and tools which automatically extract protein annotations.|
[Results] The "sentence sliding window" approach proposed here was found to efficiently extract text fragments from full text articles containing annotations on proteins, providing the highest number of correctly predicted annotations. Moreover, the number of correct extractions of individual entities (i.e. proteins and GO terms) involved in the relationships used for the annotations was significantly higher than the correct extractions of the complete annotations (protein-function relations).
[Conclusion] We explored the use of averaging sentence sliding windows for information extraction, especially in a context where conventional training data is unavailable. The combination of our approach with more refined statistical estimators and machine learning techniques might be a way to improve annotation extraction for future biomedical text mining applications.
|Descripción :||From A critical assessment of text mining methods in molecular biology|
|Aparece en las colecciones:||(CNB) Artículos|
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|1471-2105-6-S1-S19.pdf||373,2 kB||Adobe PDF|