Abstract
Acronyms are widely used in biomedical and other technical texts. Understanding their meaning constitutes an important problem in the automatic extraction and mining of information from text. Here we present a system called ACROMED that is part of a set of Information Extraction tools designed for processing and extracting information from abstracts in the Medline database. In this paper, we present the results of two strategies for finding the long forms for acronyms in biomedical texts. These strategies differ from previous automated acronym extraction methods by being tuned to the complex phrase structures of the biomedical lexicon and by incorporating shallow parsing of the text into the acronym recognition algorithm. The performance of our system was tested with several data sets obtaining a performance of 72 % recall with 97 % precision. These results are found to be better for biomedical texts than the performance of other acronym extraction systems designed for unrestricted text.