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        <title>A Multi-Task Learning Framework for Extracting Bacteria Biotope Information</title>
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    <name type="personal">
        <namePart type="given">Qi</namePart>
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        <namePart type="given">Chao</namePart>
        <namePart type="family">Liu</namePart>
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    <name type="personal">
        <namePart type="given">Ying</namePart>
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    <name type="personal">
        <namePart type="given">Xuansong</namePart>
        <namePart type="family">Xie</namePart>
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    <name type="personal">
        <namePart type="given">Xiansheng</namePart>
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        <dateIssued>2019-nov</dateIssued>
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            <title>Proceedings of The 5th Workshop on BioNLP Open Shared Tasks</title>
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    <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(Bidirectional Encoder Representations from Transformers) and pre-train it using mask language models and next sentence prediction 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.</abstract>
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    <identifier type="doi">10.18653/v1/D19-5716</identifier>
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%0 Conference Proceedings
%T A Multi-Task Learning Framework for Extracting Bacteria Biotope Information
%A Zhang, Qi
%A Liu, Chao
%A Chi, Ying
%A Xie, Xuansong
%A Hua, Xiansheng
%S Proceedings of The 5th Workshop on BioNLP Open Shared Tasks
%D 2019
%8 nov
%I Association for Computational Linguistics
%C Hong Kong, China
%F zhang-etal-2019-multi-task
%X 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(Bidirectional Encoder Representations from Transformers) and pre-train it using mask language models and next sentence prediction 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.
%R 10.18653/v1/D19-5716
%U https://aclanthology.org/D19-5716
%U https://doi.org/10.18653/v1/D19-5716
%P 105-109

Markdown (Informal)

[A Multi-Task Learning Framework for Extracting Bacteria Biotope Information](https://aclanthology.org/D19-5716) (Zhang et al., EMNLP 2019)

• A Multi-Task Learning Framework for Extracting Bacteria Biotope Information (Zhang et al., EMNLP 2019)

ACL

• Qi Zhang, Chao Liu, Ying Chi, Xuansong Xie, and Xiansheng Hua. 2019. A Multi-Task Learning Framework for Extracting Bacteria Biotope Information. In Proceedings of The 5th Workshop on BioNLP Open Shared Tasks, pages 105–109, Hong Kong, China. Association for Computational Linguistics.