NDLI: A large scale, corpus-based approach for automatically disambiguating biomedical abbreviations

Content Provider	ACM Digital Library
Author	Kim, Won Wilbur, John Yu, Hong Hatzivassiloglou, Vasileios
Copyright Year	2006
Abstract	Abbreviations and acronyms are widely used in the biomedical literature and many of them represent important biomedical concepts. Because many abbreviations are ambiguous (e.g., $\textit{CAT}$ denotes both chloramphenicol acetyl transferase and computed axial tomography, depending on the context), recognizing the full form associated with each abbreviation is in most cases equivalent to identifying the meaning of the abbreviation. This, in turn, allows us to perform more accurate natural language processing, information extraction, and retrieval. In this study, we have developed supervised approaches to identifying the full forms of ambiguous abbreviations within the context they appear. We first automatically assigned multiple possible full forms for each abbreviation; we then treated the in-context full-form prediction for each specific abbreviation occurrence as a case of word-sense disambiguation. We generated automatically a dictionary of all possible full forms for each abbreviation. We applied supervised machine-learning algorithms for disambiguation. Because some of the links between abbreviations and their corresponding full forms are explicitly given in the text and can be recovered automatically, we can use these explicit links to automatically provide training data for disambiguating the abbreviations that are not linked to a full form within a text. We evaluated our methods on over 150 thousand abstracts and obtain for coverage and precision results of 82% and 92%, respectively, when performed as tenfold cross-validation, and 79% and 80%, respectively, when evaluated against an external set of abstracts in which the abbreviations are not defined.
Starting Page	380
Ending Page	404
Page Count	25
File Format	PDF
ISSN	10468188
e-ISSN	15582868
DOI	10.1145/1165774.1165778
Volume Number	24
Issue Number	3
Journal	ACM Transactions on Information Systems (TOIS)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2006-07-01
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Word-sense disambiguation Data mining Information retrieval Machine learning
Content Type	Text
Resource Type	Article
Subject	Computer Science Applications Information Systems Business, Management and Accounting

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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