BACKGROUND There are several humanly defined ontologies relevant to Medline. However, Medline is a fast growing collection of biomedical documents which creates difficulties in updating and expanding these humanly defined ontologies. Automatically identifying meaningful categories of entities in a large text corpus is useful for information extraction, construction of machine learning features,...

We present a text mining application that exploits the MeSH heading subheading combinations present in MEDLINE records. The process begins with a user specified pair of subheadings. Co-occurring concepts qualified by these subheadings are regarded as being conceptually related and thus extracted. A parallel process using SemRep, a linguistic tool, also extracts conceptually related concept pair...

Scientific information has been growing at an exponential level, and each year over 300.000 references are added to Medline base. There is now a growing interest on the bioinformatics researches to find ways to improve the ability to find relevant documents in the literature and to extract specific kinds of information from these documents (1,2). Medline, one of the gamestones of medical scient...