LFXtractor: Text chunking for long form detection from biomedical text

Min Song, Hongfang Liu

Research output: Contribution to journalArticle

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.

Original languageEnglish
Pages (from-to)89-102
Number of pages14
JournalInternational Journal of Functional Informatics and Personalised Medicine
Volume3
Issue number2
DOIs
Publication statusPublished - 2010 Dec 1

Fingerprint

Learning

All Science Journal Classification (ASJC) codes

  • Clinical Neurology

Cite this

@article{2819f6cbae4a4c51b36d3740de952598,
title = "LFXtractor: Text chunking for long form detection from biomedical text",
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.",
author = "Min Song and Hongfang Liu",
year = "2010",
month = "12",
day = "1",
doi = "10.1504/IJFIPM.2010.037148",
language = "English",
volume = "3",
pages = "89--102",
journal = "International Journal of Functional Informatics and Personalised Medicine",
issn = "1756-2104",
publisher = "Inderscience Publishers",
number = "2",

}

LFXtractor : Text chunking for long form detection from biomedical text. / Song, Min; Liu, Hongfang.

In: International Journal of Functional Informatics and Personalised Medicine, Vol. 3, No. 2, 01.12.2010, p. 89-102.

Research output: Contribution to journalArticle

TY - JOUR

T1 - LFXtractor

T2 - Text chunking for long form detection from biomedical text

AU - Song, Min

AU - Liu, Hongfang

PY - 2010/12/1

Y1 - 2010/12/1

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

VL - 3

SP - 89

EP - 102

JO - International Journal of Functional Informatics and Personalised Medicine

JF - International Journal of Functional Informatics and Personalised Medicine

SN - 1756-2104

IS - 2

ER -