LFXtractor: Text chunking for long form detection from biomedical text
Document Type
Article
Publication Date
1-1-2010
Abstract
In this paper, we propose a novel method to detect the corresponding long forms (LFs) of short forms (SFs) from biomedical text. The proposed method is differentiated from others as follows: • it incorporates lexical analysis techniques into supervised learning for extracting abbreviations • it utilises text-chunking techniques to identify LFs of abbreviations • it significantly improves recall. The experimental results show that our approach outperforms the leading abbreviation algorithms, ExtractAbbrev, ALICE and Acrophile and a collocation-based approach at least by 4.8, 6.0, 9.0 and 6.0%, respectively, in both precision and recall on the Gold Standard Development corpus. Copyright © 2010 Inderscience Enterprises Ltd.
Identifier
84874158893 (Scopus)
Publication Title
International Journal of Functional Informatics and Personalised Medicine
External Full Text Location
https://doi.org/10.1504/IJFIPM.2010.037148
e-ISSN
17562112
ISSN
17562104
First Page
89
Last Page
102
Issue
2
Volume
3
Recommended Citation
Song, Min and Liu, Hongfang, "LFXtractor: Text chunking for long form detection from biomedical text" (2010). Faculty Publications. 6525.
https://digitalcommons.njit.edu/fac_pubs/6525
