Abstract
The number of publicly available clinical studies is constantly increasing, formulating a rather promising corpus of documents for clinical research purposes. However, the abbreviations used in these studies pose a serious barrier to any text mining technique. This paper presents a study conducted in the above domain, which used specifically developed tools and mechanisms in order to process a number of randomly selected documents from clinicaltrialsregister.eu. The analysis performed indicated that abbreviations appear at a large scale without their long form (aka expansion). In order to assess the abbreviations’ true meaning, it is necessary to utilize the appropriate corpus of documents, apply innovative algorithms and techniques to detect their possible expansions, and accordingly select the appropriate ones. Furthermore, the discrimination power of tokens has a distinctive role in abbreviations construction, and hence, it can facilitate the detection of acronym-type abbreviations. Additionally, the expressions in which abbreviations appear, as well as the preceding or following text are of primary importance for selecting the appropriate meaning.
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Acknowledgements
This work is being supported by the OpenScienceLink project [8] and has been partially funded by the European Commission’s CIP-PSP under contract number 325101. This paper expresses the opinions of the authors and not necessarily those of the European Commission. The European Commission is not liable for any use that may be made of the information contained in this paper.
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Chondrogiannis, E., Andronikou, V., Karanastasis, E., Varvarigou, T. (2015). Meaning Inference of Abbreviations Appearing in Clinical Studies. In: Sierra-Rodríguez, JL., Leal, JP., Simões, A. (eds) Languages, Applications and Technologies. SLATE 2015. Communications in Computer and Information Science, vol 563. Springer, Cham. https://doi.org/10.1007/978-3-319-27653-3_4
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DOI: https://doi.org/10.1007/978-3-319-27653-3_4
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