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    <titleInfo>
        <title>Enriching Neural Models with Targeted Features for Dementia Detection</title>
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    <name type="personal">
        <namePart type="given">Flavio</namePart>
        <namePart type="family">Di Palo</namePart>
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    <name type="personal">
        <namePart type="given">Natalie</namePart>
        <namePart type="family">Parde</namePart>
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        <dateIssued>2019-07</dateIssued>
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            <title>Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop</title>
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        <name type="personal">
            <namePart type="given">Fernando</namePart>
            <namePart type="family">Alva-Manchego</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Eunsol</namePart>
            <namePart type="family">Choi</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Daniel</namePart>
            <namePart type="family">Khashabi</namePart>
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    <abstract>Alzheimers disease is an irreversible brain disease that slowly destroys memory skills andthinking skills leading to the need for full-time care. Early detection of Alzheimer’s dis-ease is fundamental to slow down the progress of the disease. In this work we are developing Natural Language Processing techniques to detect linguistic characteristics of patients suffering Alzheimer’s Disease and related Dementias. We are proposing a neural model based on a CNN-LSTM architecture that is able to take in consideration both long language samples and hand-crafted linguistic features to distinguish between dementia affected and healthy patients. We are exploring the effects of the introduction of an attention mechanism on both our model and the actual state of the art. Our approach is able to set a new state-of-the art on the DementiaBank dataset achieving an F1 Score of 0.929 in the Dementia patients classification Supplementary material include code to run the experiments.</abstract>
    <identifier type="citekey">di-palo-parde-2019-enriching</identifier>
    <identifier type="doi">10.18653/v1/P19-2042</identifier>
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        <date>2019-07</date>
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%0 Conference Proceedings
%T Enriching Neural Models with Targeted Features for Dementia Detection
%A Di Palo, Flavio
%A Parde, Natalie
%Y Alva-Manchego, Fernando
%Y Choi, Eunsol
%Y Khashabi, Daniel
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F di-palo-parde-2019-enriching
%X Alzheimers disease is an irreversible brain disease that slowly destroys memory skills andthinking skills leading to the need for full-time care. Early detection of Alzheimer’s dis-ease is fundamental to slow down the progress of the disease. In this work we are developing Natural Language Processing techniques to detect linguistic characteristics of patients suffering Alzheimer’s Disease and related Dementias. We are proposing a neural model based on a CNN-LSTM architecture that is able to take in consideration both long language samples and hand-crafted linguistic features to distinguish between dementia affected and healthy patients. We are exploring the effects of the introduction of an attention mechanism on both our model and the actual state of the art. Our approach is able to set a new state-of-the art on the DementiaBank dataset achieving an F1 Score of 0.929 in the Dementia patients classification Supplementary material include code to run the experiments.
%R 10.18653/v1/P19-2042
%U https://aclanthology.org/P19-2042
%U https://doi.org/10.18653/v1/P19-2042
%P 302-308

Markdown (Informal)

[Enriching Neural Models with Targeted Features for Dementia Detection](https://aclanthology.org/P19-2042) (Di Palo & Parde, ACL 2019)

• Enriching Neural Models with Targeted Features for Dementia Detection (Di Palo & Parde, ACL 2019)

ACL

• Flavio Di Palo and Natalie Parde. 2019. Enriching Neural Models with Targeted Features for Dementia Detection. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop, pages 302–308, Florence, Italy. Association for Computational Linguistics.