Abstract
The automatic extraction of relations between medical entities found in related texts is considered to be a very important task, due to the multitude of applications that it can support, from question answering systems to the development of medical ontologies. Many different methodologies have been presented and applied to this task over the years. Of particular interest are hybrid approaches, in which different techniques are combined in order to improve the individual performance of either one of them. In this study, we extend a previously established hybrid framework for medical relation extraction, which we modify by enhancing the pattern-based part of the framework and by applying a more sophisticated weighting method. Most notably, we replace the use of regular expressions with finite state automata for the pattern-building part, while the fusion part is replaced by a weighting strategy that is based on the operational capabilities of the Random Forests algorithm. The experimental results indicate the superiority of the proposed approach against the aforementioned well-established hybrid methodology and other state-of-the-art approaches.
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Acknowledgments
This work was supported by the project KRISTINA (H2020-645012), funded by the European Commission. Deidentified clinical records used in this research were provided by the i2b2 National Center for Biomedical Computing funded by U54LM008748 and were originally prepared for the Shared Tasks for Challenges in NLP for Clinical Data organized by Dr. Ozlem Uzuner, i2b2 and SUNY.
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Mavropoulos, T., Liparas, D., Symeonidis, S., Vrochidis, S., Kompatsiaris, I. (2018). A Hybrid Approach for Biomedical Relation Extraction Using Finite State Automata and Random Forest-Weighted Fusion. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2017. Lecture Notes in Computer Science(), vol 10761. Springer, Cham. https://doi.org/10.1007/978-3-319-77113-7_35
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