Conceptual language models for domain-specific retrieval
Information Processing and Management: an International Journal
A cross-lingual framework for monolingual biomedical information retrieval
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Learning to annotate scientific publications
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Automatic semantic subject indexing of web documents in highly inflected languages
ESWC'11 Proceedings of the 8th extended semantic web conference on The semantic web: research and applications - Volume Part I
Quantifying the impact of concept recognition on biomedical information retrieval
Information Processing and Management: an International Journal
Journal of Biomedical Informatics
MEDLINE MeSH indexing: lessons learned from machine learning and future directions
Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium
Improving MeSH classification of biomedical articles using citation contexts
Journal of Biomedical Informatics
Improving context-based medical image retrieval by incorporating semantic-based retrieval
Proceedings of the 4th International Conference on Internet Multimedia Computing and Service
Grading the quality of medical evidence
BioNLP '12 Proceedings of the 2012 Workshop on Biomedical Natural Language Processing
Merging words and concepts for medical articles retrieval
Proceedings of the 10th Conference on Open Research Areas in Information Retrieval
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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 subset of MeSH or have only been compared with a limited number of other systems. Results: We compare the performance of six MeSH classification systems [MetaMap, EAGL, a language and a vector space model-based approach, a K-Nearest Neighbor (KNN) approach and MTI] in terms of reproducing and complementing manual MeSH annotations. A KNN system clearly outperforms the other published approaches and scales well with large amounts of text using the full MeSH thesaurus. Our measurements demonstrate to what extent manual MeSH annotations can be reproduced and how they can be complemented by automatic annotations. We also show that a statistically significant improvement can be obtained in information retrieval (IR) when the text of a user's query is automatically annotated with MeSH concepts, compared to using the original textual query alone. Conclusions: The annotation of biomedical texts using controlled vocabularies such as MeSH can be automated to improve text-only IR. Furthermore, the automatic MeSH annotation system we propose is highly scalable and it generates improvements in IR comparable with those observed for manual annotations. Contact: trieschn@ewi.utwente.nl Supplementary information: Supplementary data are available at Bioinformatics online.