@inproceedings{karadeniz-etal-2019-boun,
    title = "{BOUN}-{ISIK} Participation: An Unsupervised Approach for the Named Entity Normalization and Relation Extraction of Bacteria Biotopes",
    author = {Karadeniz, {\.I}lknur  and
      Tuna, {\"O}mer Faruk  and
      {\"O}zg{\"u}r, Arzucan},
    editor = "Jin-Dong, Kim  and
      Claire, N{\'e}dellec  and
      Robert, Bossy  and
      Louise, Del{\'e}ger",
    booktitle = "Proceedings of the 5th Workshop on BioNLP Open Shared Tasks",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/D19-5722/",
    doi = "10.18653/v1/D19-5722",
    pages = "150--157",
    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."
}