Knowledge-intensive conceptual retrieval and passage extraction of biomedical literature
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
A system for finding biological entities that satisfy certain conditions from texts
Proceedings of the 17th ACM conference on Information and knowledge management
Exploring criteria for successful query expansion in the genomic domain
Information Retrieval
Computer Methods and Programs in Biomedicine
Disambiguation of biomedical abbreviations
BioNLP '09 Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing
A discriminative alignment model for abbreviation recognition
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Automated identification of synonyms in biomedical acronym sense inventories
Louhi '10 Proceedings of the NAACL HLT 2010 Second Louhi Workshop on Text and Data Mining of Health Documents
Disambiguating biomedical acronyms using EMIM
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Artificial Intelligence in Medicine
Alignment-HMM-based extraction of abbreviations from biomedical text
BioNLP '12 Proceedings of the 2012 Workshop on Biomedical Natural Language Processing
A new clustering method for detecting rare senses of abbreviations in clinical notes
Journal of Biomedical Informatics
Learning Abbreviations from Chinese and English Terms by Modeling Non-Local Information
ACM Transactions on Asian Language Information Processing (TALIP)
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Motivation: Abbreviations are an important type of terminology in the biomedical domain. Although several groups have already created databases of biomedical abbreviations, these are either not public, or are not comprehensive, or focus exclusively on acronym-type abbreviations. We have created another abbreviation database, ADAM, which covers commonly used abbreviations and their definitions (or long-forms) within MEDLINE titles and abstracts, including both acronym and non-acronym abbreviations. Results: A model of recognizing abbreviations and their long-forms from titles and abstracts of MEDLINE (2006 baseline) was employed. After grouping morphological variants, 59 405 abbreviation/long-form pairs were identified. ADAM shows high precision (97.4%) and includes most of the frequently used abbreviations contained in the Unified Medical Language System (UMLS) Lexicon and the Stanford Abbreviation Database. Conversely, one-third of abbreviations in ADAM are novel insofar as they are not included in either database. About 19% of the novel abbreviations are non-acronym-type and these cover at least seven different types of short-form/long-form pairs. Availability: A free, public query interface to ADAM is available at http://arrowsmith.psych.uic.edu, and the entire database can be downloaded as a text file. Contact: neils@uic.edu