Abstract
With the continuous digitisation of medical knowledge, information extraction tools become more and more important for practitioners of the medical domain. In this paper we tackle semantic relationships extraction from medical texts. We focus on the relations that may occur between diseases and treatments. We propose an approach relying on two different techniques to extract the target relations: (i) relation patterns based on human expertise and (ii) machine learning based on SVM classification. The presented approach takes advantage of the two techniques, relying more on manual patterns when few relation examples are available and more on feature values when a sufficient number of examples are available. Our approach obtains an overall 94.07% F-measure for the extraction of cure, prevent and side effect relations.
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Ben Abacha, A., Zweigenbaum, P. (2011). A Hybrid Approach for the Extraction of Semantic Relations from MEDLINE Abstracts. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2011. Lecture Notes in Computer Science, vol 6609. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19437-5_11
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DOI: https://doi.org/10.1007/978-3-642-19437-5_11
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