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    Language model-based automatic prefix abbreviation expansion method for biomedical big data analysis

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    Authors
    Anjum, Ashiq
    Affiliation
    University of Derby
    Issue Date
    2019-03-28
    
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    Abstract
    In biomedical domain, abbreviations are appearing more and more frequently in various data sets, which has caused significant obstacles to biomedical big data analysis. The dictionary-based approach has been adopted to process abbreviations, but it cannot handle ad hoc abbreviations, and it is impossible to cover all abbreviations. To overcome these drawbacks, this paper proposes an automatic abbreviation expansion method called LMAAE (Language Model-based Automatic Abbreviation Expansion). In this method, the abbreviation is firstly divided into blocks; then, expansion candidates are generated by restoring each block; and finally, the expansion candidates are filtered and clustered to acquire the final expansion result according to the language model and clustering method. Through restrict the abbreviation to prefix abbreviation, the search space of expansion is reduced sharply. And then, the search space is continuous reduced by restrained the effective and the length of the partition. In order to validate the effective of the method, two types of experiments are designed. For standard abbreviations, the expansion results include most of the expansion in dictionary. Therefore, it has a high precision. For ad hoc abbreviations, the precisions of schema matching, knowledge fusion are increased by using this method to handle the abbreviations. Although the recall for standard abbreviation needs to be improved, but this does not affect the good complement effect for the dictionary method.
    Citation
    Du, X., Zhu, R., Li, Y. and Anjum, A., (2019). 'Language model-based automatic prefix abbreviation expansion method for biomedical big data analysis'. Future Generation Computer Systems, 98, pp. 238-251. DOI: 10.1016/j.future.2019.01.016.
    Publisher
    Elsevier
    Journal
    Future Generation Computer Systems
    URI
    http://hdl.handle.net/10545/623968
    DOI
    10.1016/j.future.2019.01.016
    Additional Links
    https://www.sciencedirect.com/science/article/pii/S0167739X18326529
    Type
    Article
    Language
    en
    ISSN
    0167739X
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.future.2019.01.016
    Scopus Count
    Collections
    Department of Electronics, Computing & Maths

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