Approximate matching-based unsupervised document indexing approach: application to biomedical domain

Document indexing is considered as a crucial phase in the information retrieval field because textual information is constantly increasing. With this accumulation of documents, the satisfaction of user needs becomes more and more complex. For these reasons, several information retrieval systems have been designed in order to respond to user requests. The main contribution of the current work resides in the suggestion of a novel hybrid approach for biomedical document indexing. We improve the estimation of the correspondence between a document and a given concept using two methods: vector space model (VSM) and description logics (DL). VSM performs partial matching between documents and external resource terms. DL allows representing knowledge in a relevant manner for better matching. The proposed contribution reduces the limitation of exact matching. It serves to index documents by exploiting medical subject headings (MeSH) thesaurus services with approximate matching. The latter partially matches document terms with biomedical vocabularies to extract other morphological variants in that resource. It also generates irrelevant concepts. The filtering step solves this problem and grants the selection of the most important concepts by exploiting the knowledge provided by MeSH. The experiments, carried out on different corpora, show encouraging results of around 25% improvement in average accuracy compared to other approaches studied in the literature.