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	title        = {Medical Event Extraction using Frame Semantics - Challenges and Opportunities. Samos, Greece},
	abstract     = {Abstract. The aim of this paper is to present some findings from a study into how a large scale semantic resource, FrameNet, can be applied for event extraction in the (Swedish) biomedical domain. Combining lexical resources with domain specific knowledge provide a powerful modeling mechanism that can be utilized for event extraction and other advanced text mining-related activities. The results, from developing a rule-based approach, showed that only small discrepancies and omissions were found between the semantic descriptions, the corpus data examined and the domain-specific semantics provided by SNOMED CT (medical terminology), NPL (medicinal products) and various semi-automatically developed clue lists (e. g., domain-related abbreviations). Although the described experiment is only based on four different domain-specific frames, the methodology is extendable to the rest ones and there is much room for improvements, for instance by combining rule-based with machine learning techniques, and using more advanced syntactic representations.},
	booktitle    = {Proceedings of the 14th International Conference on Intelligent Text Processing and Computational Linguistics (CICLing)},
	author       = {Kokkinakis, Dimitrios},
	year         = {2013},