BOUN-ISIK participation: an unsupervised approach for the named entity normalization and relation extraction of Bacteria Biotopes
Yükleniyor...
Tarih
2019-11-04
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Association for Computational Linguistics (ACL)
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
This paper presents our participation at 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.
Açıklama
Anahtar Kelimeler
Computational linguistics, Deep learning, Extraction, Natural language processing systems, Biomedical text, Embeddings, Labeled data, Learning-based methods, Named entity normalizations, Normalisation, Re-ranking, Relation extraction, Subtask, Unsupervised approaches, Bacteria, Biomedical, Curation, Medline
Kaynak
Proceedings of the 5th Workshop on BioNLP Open Shared Tasks
WoS Q Değeri
Scopus Q Değeri
N/A
Cilt
Sayı
Künye
Karadeniz, İ., Tuna, Ö. F. & Özgu, A. (2019). BOUN-ISIK participation: an unsupervised approach for the named entity normalization and relation extraction of Bacteria Biotopes. Paper presented at the Proceedings of the 5th Workshop on BioNLP Open Shared Tasks, 150-157.
