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        <title>An Insight Extraction System on BioMedical Literature with Deep Neural Networks</title>
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    <name type="personal">
        <namePart type="given">Hua</namePart>
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    <name type="personal">
        <namePart type="given">Navendu</namePart>
        <namePart type="family">Jain</namePart>
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    <name type="personal">
        <namePart type="given">Jessica</namePart>
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            <namePart type="given">Rebecca</namePart>
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    <abstract>Mining biomedical text offers an opportunity to automatically discover important facts and infer associations among them. As new scientific findings appear across a large collection of biomedical publications, our aim is to tap into this literature to automate biomedical knowledge extraction and identify important insights from them. Towards that goal, we develop a system with novel deep neural networks to extract insights on biomedical literature. Evaluation shows our system is able to provide insights with competitive accuracy of human acceptance and its relation extraction component outperforms previous work.</abstract>
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%0 Conference Proceedings
%T An Insight Extraction System on BioMedical Literature with Deep Neural Networks
%A He, Hua
%A Ganjam, Kris
%A Jain, Navendu
%A Lundin, Jessica
%A White, Ryen
%A Lin, Jimmy
%Y Palmer, Martha
%Y Hwa, Rebecca
%Y Riedel, Sebastian
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F he-etal-2017-insight
%X Mining biomedical text offers an opportunity to automatically discover important facts and infer associations among them. As new scientific findings appear across a large collection of biomedical publications, our aim is to tap into this literature to automate biomedical knowledge extraction and identify important insights from them. Towards that goal, we develop a system with novel deep neural networks to extract insights on biomedical literature. Evaluation shows our system is able to provide insights with competitive accuracy of human acceptance and its relation extraction component outperforms previous work.
%R 10.18653/v1/D17-1285
%U https://aclanthology.org/D17-1285
%U https://doi.org/10.18653/v1/D17-1285
%P 2691-2701

Markdown (Informal)

[An Insight Extraction System on BioMedical Literature with Deep Neural Networks](https://aclanthology.org/D17-1285) (He et al., EMNLP 2017)

• An Insight Extraction System on BioMedical Literature with Deep Neural Networks (He et al., EMNLP 2017)

ACL

• Hua He, Kris Ganjam, Navendu Jain, Jessica Lundin, Ryen White, and Jimmy Lin. 2017. An Insight Extraction System on BioMedical Literature with Deep Neural Networks. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 2691–2701, Copenhagen, Denmark. Association for Computational Linguistics.