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        <title>Is artificial data useful for biomedical Natural Language Processing algorithms?</title>
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    <name type="personal">
        <namePart type="given">Zixu</namePart>
        <namePart type="family">Wang</namePart>
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    <name type="personal">
        <namePart type="given">Julia</namePart>
        <namePart type="family">Ive</namePart>
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    <name type="personal">
        <namePart type="given">Sumithra</namePart>
        <namePart type="family">Velupillai</namePart>
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    <name type="personal">
        <namePart type="given">Lucia</namePart>
        <namePart type="family">Specia</namePart>
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            <title>Proceedings of the 18th BioNLP Workshop and Shared Task</title>
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        <name type="personal">
            <namePart type="given">Dina</namePart>
            <namePart type="family">Demner-Fushman</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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            <namePart type="given">Kevin</namePart>
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            <namePart type="family">Cohen</namePart>
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            <namePart type="given">Sophia</namePart>
            <namePart type="family">Ananiadou</namePart>
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        <name type="personal">
            <namePart type="given">Junichi</namePart>
            <namePart type="family">Tsujii</namePart>
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    <abstract>A major obstacle to the development of Natural Language Processing (NLP) methods in the biomedical domain is data accessibility. This problem can be addressed by generating medical data artificially. Most previous studies have focused on the generation of short clinical text, and evaluation of the data utility has been limited. We propose a generic methodology to guide the generation of clinical text with key phrases. We use the artificial data as additional training data in two key biomedical NLP tasks: text classification and temporal relation extraction. We show that artificially generated training data used in conjunction with real training data can lead to performance boosts for data-greedy neural network algorithms. We also demonstrate the usefulness of the generated data for NLP setups where it fully replaces real training data.</abstract>
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    <identifier type="doi">10.18653/v1/W19-5026</identifier>
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        <url>https://aclanthology.org/W19-5026</url>
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        <date>2019-08</date>
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%0 Conference Proceedings
%T Is artificial data useful for biomedical Natural Language Processing algorithms?
%A Wang, Zixu
%A Ive, Julia
%A Velupillai, Sumithra
%A Specia, Lucia
%Y Demner-Fushman, Dina
%Y Cohen, Kevin Bretonnel
%Y Ananiadou, Sophia
%Y Tsujii, Junichi
%S Proceedings of the 18th BioNLP Workshop and Shared Task
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F wang-etal-2019-artificial
%X A major obstacle to the development of Natural Language Processing (NLP) methods in the biomedical domain is data accessibility. This problem can be addressed by generating medical data artificially. Most previous studies have focused on the generation of short clinical text, and evaluation of the data utility has been limited. We propose a generic methodology to guide the generation of clinical text with key phrases. We use the artificial data as additional training data in two key biomedical NLP tasks: text classification and temporal relation extraction. We show that artificially generated training data used in conjunction with real training data can lead to performance boosts for data-greedy neural network algorithms. We also demonstrate the usefulness of the generated data for NLP setups where it fully replaces real training data.
%R 10.18653/v1/W19-5026
%U https://aclanthology.org/W19-5026
%U https://doi.org/10.18653/v1/W19-5026
%P 240-249

Markdown (Informal)

[Is artificial data useful for biomedical Natural Language Processing algorithms?](https://aclanthology.org/W19-5026) (Wang et al., BioNLP 2019)

• Is artificial data useful for biomedical Natural Language Processing algorithms? (Wang et al., BioNLP 2019)

ACL

• Zixu Wang, Julia Ive, Sumithra Velupillai, and Lucia Specia. 2019. Is artificial data useful for biomedical Natural Language Processing algorithms?. In Proceedings of the 18th BioNLP Workshop and Shared Task, pages 240–249, Florence, Italy. Association for Computational Linguistics.