Suwisa Kaewphan


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End-to-End System for Bacteria Habitat Extraction
Farrokh Mehryary | Kai Hakala | Suwisa Kaewphan | Jari Björne | Tapio Salakoski | Filip Ginter
BioNLP 2017

We introduce an end-to-end system capable of named-entity detection, normalization and relation extraction for extracting information about bacteria and their habitats from biomedical literature. Our system is based on deep learning, CRF classifiers and vector space models. We train and evaluate the system on the BioNLP 2016 Shared Task Bacteria Biotope data. The official evaluation shows that the joint performance of our entity detection and relation extraction models outperforms the winning team of the Shared Task by 19pp on F1-score, establishing a new top score for the task. We also achieve state-of-the-art results in the normalization task. Our system is open source and freely available at


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Syntactic analyses and named entity recognition for PubMed and PubMed Central — up-to-the-minute
Kai Hakala | Suwisa Kaewphan | Tapio Salakoski | Filip Ginter
Proceedings of the 15th Workshop on Biomedical Natural Language Processing


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UTU: Disease Mention Recognition and Normalization with CRFs and Vector Space Representations
Suwisa Kaewphan | Kai Hakala | Filip Ginter
Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)


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UTurku: Drug Named Entity Recognition and Drug-Drug Interaction Extraction Using SVM Classification and Domain Knowledge
Jari Björne | Suwisa Kaewphan | Tapio Salakoski
Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013)

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Evaluating Large-scale Text Mining Applications Beyond the Traditional Numeric Performance Measures
Sofie Van Landeghem | Suwisa Kaewphan | Filip Ginter | Yves Van de Peer
Proceedings of the 2013 Workshop on Biomedical Natural Language Processing