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        <title>Integration of Deep Learning and Traditional Machine Learning for Knowledge Extraction from Biomedical Literature</title>
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        <namePart type="given">Jihang</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>In this paper, we present our participation in the Bacteria Biotope (BB) task at BioNLP-OST 2019. Our system utilizes fine-tuned language representation models and machine learning approaches based on word embedding and lexical features for entities recognition, normalization and relation extraction. It achieves the state-of-the-art performance and is among the top two systems in five of all six subtasks.</abstract>
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    <identifier type="doi">10.18653/v1/D19-5724</identifier>
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%0 Conference Proceedings
%T Integration of Deep Learning and Traditional Machine Learning for Knowledge Extraction from Biomedical Literature
%A Mao, Jihang
%A Liu, Wanli
%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 mao-liu-2019-integration
%X In this paper, we present our participation in the Bacteria Biotope (BB) task at BioNLP-OST 2019. Our system utilizes fine-tuned language representation models and machine learning approaches based on word embedding and lexical features for entities recognition, normalization and relation extraction. It achieves the state-of-the-art performance and is among the top two systems in five of all six subtasks.
%R 10.18653/v1/D19-5724
%U https://aclanthology.org/D19-5724
%U https://doi.org/10.18653/v1/D19-5724
%P 168-173

Markdown (Informal)

[Integration of Deep Learning and Traditional Machine Learning for Knowledge Extraction from Biomedical Literature](https://aclanthology.org/D19-5724) (Mao & Liu, EMNLP 2019)

• Integration of Deep Learning and Traditional Machine Learning for Knowledge Extraction from Biomedical Literature (Mao & Liu, EMNLP 2019)

ACL

• Jihang Mao and Wanli Liu. 2019. Integration of Deep Learning and Traditional Machine Learning for Knowledge Extraction from Biomedical Literature. In Proceedings of The 5th Workshop on BioNLP Open Shared Tasks, pages 168–173, Hong Kong, China. Association for Computational Linguistics.