MeSH Up: effective MeSH text classification for improved document retrieval

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MeSH Up: effective MeSH text classification for improved document retrieval

MOTIVATION Controlled vocabularies such as the Medical Subject Headings (MeSH) thesaurus and the Gene Ontology (GO) provide an efficient way of accessing and organizing biomedical information by reducing the ambiguity inherent to free-text data. Different methods of automating the assignment of MeSH concepts have been proposed to replace manual annotation, but they are either limited to a small...

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Response to comment on 'MeSH-up: effective MeSH text classification for improved document retrieval'

In response to the methodological considerations, we emphasize that in our paper we compare different MeSH classification systems on two tasks: (i) reproducing manual MeSH recommendations (referred to as indexing by Névéol et al.) and (ii) translating a textual query to an additional MeSH representation (referred to as query expansion). We show that the approach we propose works well on both ta...

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Comment on 'MeSH-up: effective MeSH text classification for improved document retrieval'

Information retrieval is an important task that requires specific attention in the biomedical domain where controlled vocabularies are available to characterize and organize textual content. A recent article published in Bioinformatics (Trieschnigg et al., 2009) confirms that there is a continued interest in the community to address this problem and achieve ‘improved document retrieval’. As sho...

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Comment on ‘MeSH-up: effective MeSH text classification for

Information retrieval is an important task that requires specific attention in the biomedical domain where controlled vocabularies are available to characterize and organize textual content. A recent article published in Bioinformatics (Trieschnigg et al., 2009) confirms that there is a continued interest in the community to address this problem and achieve ‘improved document retrieval’. As sho...

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MeSH: a window into full text for document summarization

MOTIVATION Previous research in the biomedical text-mining domain has historically been limited to titles, abstracts and metadata available in MEDLINE records. Recent research initiatives such as TREC Genomics and BioCreAtIvE strongly point to the merits of moving beyond abstracts and into the realm of full texts. Full texts are, however, more expensive to process not only in terms of resources...

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ژورنال

عنوان ژورنال: Bioinformatics

سال: 2009

ISSN: 1460-2059,1367-4803

DOI: 10.1093/bioinformatics/btp249