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 language | English (US) |
|---|---|
| Pages (from-to) | 89-102 |
| Number of pages | 14 |
| Journal | International Journal of Functional Informatics and Personalised Medicine |
| Volume | 3 |
| Issue number | 2 |
| DOIs | |
| State | Published - 2010 |
Keywords
- Abbreviation extraction
- Text chunking
- Text mining
ASJC Scopus subject areas
- Clinical Neurology
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