TY - JOUR
T1 - LFXtractor
T2 - Text chunking for long form detection from biomedical text
AU - Song, Min
AU - Liu, Hongfang
PY - 2010
Y1 - 2010
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84874158893&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84874158893&partnerID=8YFLogxK
U2 - 10.1504/IJFIPM.2010.037148
DO - 10.1504/IJFIPM.2010.037148
M3 - Article
AN - SCOPUS:84874158893
SN - 1756-2104
VL - 3
SP - 89
EP - 102
JO - International Journal of Functional Informatics and Personalised Medicine
JF - International Journal of Functional Informatics and Personalised Medicine
IS - 2
ER -