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        <title>BOUN-ISIK Participation: An Unsupervised Approach for the Named Entity Normalization and Relation Extraction of Bacteria Biotopes</title>
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        <namePart type="given">İlknur</namePart>
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        <namePart type="given">Ömer</namePart>
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        <namePart type="family">Tuna</namePart>
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            <title>Proceedings of the 5th Workshop on BioNLP Open Shared Tasks</title>
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            <namePart type="given">Kim</namePart>
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    <abstract>This paper presents our participation to the Bacteria Biotope Task of the BioNLP Shared Task 2019. Our participation includes two systems for the two subtasks of the Bacteria Biotope Task: the normalization of entities (BB-norm) and the identification of the relations between the entities given a biomedical text (BB-rel). For the normalization of entities, we utilized word embeddings and syntactic re-ranking. For the relation extraction task, pre-defined rules are used. Although both approaches are unsupervised, in the sense that they do not need any labeled data, they achieved promising results. Especially, for the BB-norm task, the results have shown that the proposed method performs as good as deep learning based methods, which require labeled data.</abstract>
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%0 Conference Proceedings
%T BOUN-ISIK Participation: An Unsupervised Approach for the Named Entity Normalization and Relation Extraction of Bacteria Biotopes
%A Karadeniz, İlknur
%A Tuna, Ömer Faruk
%A Özgür, Arzucan
%Y Jin-Dong, Kim
%Y Claire, Nédellec
%Y Robert, Bossy
%Y Louise, Deléger
%S Proceedings of the 5th Workshop on BioNLP Open Shared Tasks
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F karadeniz-etal-2019-boun
%X This paper presents our participation to the Bacteria Biotope Task of the BioNLP Shared Task 2019. Our participation includes two systems for the two subtasks of the Bacteria Biotope Task: the normalization of entities (BB-norm) and the identification of the relations between the entities given a biomedical text (BB-rel). For the normalization of entities, we utilized word embeddings and syntactic re-ranking. For the relation extraction task, pre-defined rules are used. Although both approaches are unsupervised, in the sense that they do not need any labeled data, they achieved promising results. Especially, for the BB-norm task, the results have shown that the proposed method performs as good as deep learning based methods, which require labeled data.
%R 10.18653/v1/D19-5722
%U https://aclanthology.org/D19-5722
%U https://doi.org/10.18653/v1/D19-5722
%P 150-157

Markdown (Informal)

[BOUN-ISIK Participation: An Unsupervised Approach for the Named Entity Normalization and Relation Extraction of Bacteria Biotopes](https://aclanthology.org/D19-5722) (Karadeniz et al., BioNLP 2019)

• BOUN-ISIK Participation: An Unsupervised Approach for the Named Entity Normalization and Relation Extraction of Bacteria Biotopes (Karadeniz et al., BioNLP 2019)

ACL

• İlknur Karadeniz, Ömer Faruk Tuna, and Arzucan Özgür. 2019. BOUN-ISIK Participation: An Unsupervised Approach for the Named Entity Normalization and Relation Extraction of Bacteria Biotopes. In Proceedings of the 5th Workshop on BioNLP Open Shared Tasks, pages 150–157, Hong Kong, China. Association for Computational Linguistics.