Identifying Abbreviations in Biomedical Literature Based on Maximum Entropy with Web Features

Jing Peng; Yan Wang; Hong Min Sun

doi:10.4028/www.scientific.net/AMR.998-999.1024

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 998-999Identifying Abbreviations in Biomedical Literature...

Identifying Abbreviations in Biomedical Literature Based on Maximum Entropy with Web Features

Abstract:

The number of biomedical literatures is growing rapidly, and biomedical literature mining is becoming essential. A learning classifier based on maximum entropy (ME) for identifying abbreviations is proposed. Two innovative Web-based features for extracting additional semantic information are developed. The study shows the Web as a knowledge source can be incorporated effectively in the machine learning framework and significantly improves its performance. The ME classifier achieves 95% precision and 89% recall on the gold standard corpus “Medstract” and 91% precision and 84% recall on the larger test data that includes 128 full text literatures.

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Periodical:

Advanced Materials Research (Volumes 998-999)

Pages:

1024-1027

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.998-999.1024

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Online since:

July 2014

Authors:

Jing Peng*, Yan Wang, Hong Min Sun

Keywords:

Abbreviation Recognition, Biomedical Literature Mining, ME Classifier, Text Mining

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