A Multi-Task Learning Framework for Extracting Bacteria Biotope Information

7Citations
Citations of this article
72Readers
Mendeley users who have this article in their library.

Abstract

This paper presents a novel transfer multi-task learning method for Bacteria Biotope rel+ner task at BioNLP-OST 2019. To alleviate the data deficiency problem in domain-specific information extraction, we use BERT(Devlin et al., 2018) (Bidirectional Encoder Representations from Transformers) and pre-train it using mask language models and next sentence prediction (Devlin et al., 2018) on both general corpus and medical corpus like PubMed. In fine-tuning stage, we fine-tune the relation extraction layer and mention recognition layer designed by us on the top of BERT to extract mentions and relations simultaneously. The evaluation results show that our method achieves the best performance on all metrics (including slot error rate, precision and recall) in the Bacteria Biotope rel+ner subtask. c 2019 Association for Computational Linguistics.

Cite

CITATION STYLE

APA

Zhang, Q., Liu, C., Chi, Y., Xie, X., & Hua, X. (2019). A Multi-Task Learning Framework for Extracting Bacteria Biotope Information. In BioNLP-OST@EMNLP-IJNCLP 2019 - Proceedings of the 5th Workshop on BioNLP Open Shared Tasks (pp. 105–109). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/d19-5716

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free