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A Hybrid approach for biomedical relation extraction using finite state automata and random forest-weighted fusion

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dc.contributor.author Mavropoulos, Thanassis
dc.contributor.author Liparas, Dimitris
dc.contributor.author Symeonidis, Spyridon
dc.contributor.author Vrochidis, Stefanos
dc.contributor.author Kompatsiaris, Ioannis
dc.date.accessioned 2018-04-06T17:08:38Z
dc.date.available 2018-04-06T17:08:38Z
dc.date.issued 2017
dc.identifier.citation Mavropoulos T, Liparas D, Symeonidis S, Vrochidis S, Kompatsiaris I. A Hybrid approach for biomedical relation extraction using finite state automata and random forest-weighted fusion. Paper presented at: 18th International Conference on Computational Linguistics and Intelligent Text Processing (CICLing 2017); 2017 April 17-23; Budapest, Hungary. [13 p.].
dc.identifier.uri http://hdl.handle.net/10230/34304
dc.description Comunicació presentada a: The 18th International Conference on Computational Linguistics and Intelligent Text Processing (CICLing 2017), celebrada a Budapest, Hungria, del 17 al 23 d'abril de 2017.
dc.description.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 devel-opment of medical ontologies. Many different methodologies have been pre-sented 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 previ-ously 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 regu-lar 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.
dc.description.sponsorship 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.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Springer
dc.title A Hybrid approach for biomedical relation extraction using finite state automata and random forest-weighted fusion
dc.type info:eu-repo/semantics/conferenceObject
dc.subject.keyword Natural Language Processing
dc.subject.keyword Relation extraction
dc.subject.keyword Supervised learning
dc.subject.keyword Support vector machines
dc.subject.keyword Random forests
dc.subject.keyword Weighted fusion
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/645012
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/acceptedVersion


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