Combining NLP and probabilistic categorisation for document and term selection for Swiss-Prot medical annotation
Combining NLP and probabilistic categorisation for document and term selection for Swiss-Prot medical annotation
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DOI：
10.1093/bioinformatics/btg1011
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发表时间：
2003-07-01
期刊：
影响因子：
5.8
通讯作者：
Gaussier, Eric
中科院分区：
文献类型：
--
作者：
Dobrokhotov, Pavel B.;Goutte, Cyril;Gaussier, Eric
Motivation: Searching relevant publications for manual database annotation is a tedious task. In this paper, we apply a combination of Natural Language Processing (NLP) and probabilistic classification to re-rank documents returned by PubMed according to their relevance to SwissProt annotation, and to identify significant terms in the documents.Results: With a Probabilistic Latent Categoriser (PLC) we obtained 69% recall and 59% precision for relevant documents in a representative query. As the PLC technique provides the relative contribution of each term to the final document score, we used the Kullback-Leibler symmetric divergence to determine the most discriminating words for Swiss-Prot medical annotation. This information should allow curators to understand classification results better. It also has great value for fine-tuning the linguistic preprocessing of documents, which in turn can improve the overall classifier performance.
