NDLI: A Language-Independent Acronym Extraction From Biomedical Texts With Hidden Markov Models

Content Provider	IEEE Xplore Digital Library
Author	Osiek, B.A. Xexéo, G. de Carvalho, L.A.V.
Copyright Year	1964
Abstract	This paper proposes to model the extraction of acronyms and their meaning from unstructured text as a stochastic process using hidden Markov models (HMMs). The underlying, or hidden, chain is derived from the acronym, where the states in the chain are made by the acronyms characters. The transition between two states happens when the origin state emits a signal. Signals recognizable by the HMM are the tokens extracted from text. Observations are the sequence of tokens also extracted from text. Given a set of observations, the acronym definition will be the observation with the highest probability to emerge from the HMM. Modeling this extraction probabilistically allows us to deal with two difficult aspects of this process: ambiguity and noise. We characterize ambiguity when there is no unique alignment between the characters in the acronym with a token in the expansion, while the feature-characterizing noise is the absence of such alignment. Our experiments have proven that this approach has high precision (93.50%) and recall (85.50%) rates in an environment, where acronym coinage is ambiguous and noisy, such as the biomedical domain. Processing and comparing the approached described in this paper with different others showed that the former reaches the highest F1 score (89.40%) on the same corpus.
Sponsorship	IEEE Engineering in Medicine and Biology Society
Page Count	12
File Size	273584
Starting Page	2677
Ending Page	2688
File Format	PDF
ISSN	00189294
Volume Number	57
Issue Number	11
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-11-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hidden Markov models Data mining Working environment noise Brazil Council Text recognition Permission Stochastic processes Text analysis Computer science Dictionaries information extraction Acronym extraction biomedical text analysis hidden Markov models (HMMs)
Content Type	Text
Resource Type	Article
Subject	Biomedical Engineering

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