Authors create abbreviations in many different ways, as summarized in Table 1 ▶. Using such a strict definition excludes many types of abbreviations that appear in biomedical literature. Although the term acronym appears more commonly in literature, it is typically defined more strictly as a conjunction of the initial letter of words some authors also require them to be pronounceable. This article defines abbreviation broadly to include all strings that are shortened forms of sequences of words (its long form). An automatic method to define abbreviations would help researchers by providing a self-updating abbreviation dictionary and also facilitate computer analysis of text. Understanding biomedical literature is particularly challenging because of its expanding vocabulary, including the unfettered introduction of new abbreviations.
To alleviate this problem, the biomedical informatics community is investigating methods to organize, 1 summarize, 2 and mine 3 the literature. With biomedical knowledge expanding so quickly, professionals must acquire new strategies to cope with it. The amount of literature in biomedicine is exploding as MEDLINE grows by 400,000 citations each year. We are making this available as a public abbreviation server at \url. We have developed an algorithm to identify abbreviations from text. Of all the abbreviations in the list from the China Medical Tribune, 88% were in the database.Ĭonclusion. Applying the algorithm to all of MEDLINE yielded a database of 781,632 high-scoring abbreviations. On the Medstract corpus, our algorithm achieves up to 83% recall at 80% precision. We also measured the recall when searching for abbreviations from the China Medical Tribune against the database. We measured the recall and precision of the algorithm in identifying abbreviations from the Medstract corpus. To test the coverage of the database, we used an independently created list of abbreviations from the China Medical Tribune. We then ran the algorithm on all abstracts in MEDLINE, creating a dictionary of biomedical abbreviations. We applied it to Medstract, a corpus of MEDLINE abstracts in which abbreviations and their expansions have been manually annotated. Our method uses a statistical learning algorithm, logistic regression, to score abbreviation expansions based on their resemblance to a training set of human-annotated abbreviations. Therefore, to create an automatically generated and maintained lexicon of abbreviations, we have developed an algorithm to match abbreviations in text with their expansions.ĭesign. Each additional abbreviation increases the effective size of the vocabulary for a field.
One such challenge derives from the common and uncontrolled use of abbreviations in the literature. The growth of the biomedical literature presents special challenges for both human readers and automatic algorithms.
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