Multi-task learning for interpretable cause of death classification using key phrase prediction

Serena Jeblee; Mireille Gomes; Graeme Hirst

doi:10.18653/v1/W18-2302

Multi-task learning for interpretable cause of death classification using key phrase prediction

Serena Jeblee, Mireille Gomes, Graeme Hirst

Correct Metadata for

Important: The Anthology treat PDFs as authoritative. Please use this form only to correct data that is out of line with the PDF. See our corrections guidelines if you need to change the PDF.

Title Adjust the title. Retain tags such as <fixed-case>.

Authors Adjust author names and order to match the PDF.

Abstract Correct abstract if needed. Retain XML formatting tags such as <tex-math>.

Verification against PDF Ensure that the new title/authors match the snapshot below. (If there is no snapshot or it is too small, consult the PDF.)

Authors concatenated from the text boxes above:

ALL author names match the snapshot above—including middle initials, hyphens, and accents.

Abstract

We introduce a multi-task learning model for cause-of-death classification of verbal autopsy narratives that jointly learns to output interpretable key phrases. Adding these key phrases outperforms the baseline model and topic modeling features.

Anthology ID:: W18-2302
Volume:: Proceedings of the BioNLP 2018 workshop
Month:: July
Year:: 2018
Address:: Melbourne, Australia
Editors:: Dina Demner-Fushman, Kevin Bretonnel Cohen, Sophia Ananiadou, Junichi Tsujii
Venue:: BioNLP
SIG:
Publisher:: Association for Computational Linguistics
Note:
Pages:: 12–17
Language:
URL:: https://aclanthology.org/W18-2302/
DOI:: 10.18653/v1/W18-2302
Bibkey:
Cite (ACL):: Serena Jeblee, Mireille Gomes, and Graeme Hirst. 2018. Multi-task learning for interpretable cause of death classification using key phrase prediction. In Proceedings of the BioNLP 2018 workshop, pages 12–17, Melbourne, Australia. Association for Computational Linguistics.
Cite (Informal):: Multi-task learning for interpretable cause of death classification using key phrase prediction (Jeblee et al., BioNLP 2018)
Copy Citation:
PDF:: https://aclanthology.org/W18-2302.pdf

PDF Cite Search Fix data

Export citation

BibTeX
MODS XML
Endnote
Preformatted

@inproceedings{jeblee-etal-2018-multi,
    title = "Multi-task learning for interpretable cause of death classification using key phrase prediction",
    author = "Jeblee, Serena  and
      Gomes, Mireille  and
      Hirst, Graeme",
    editor = "Demner-Fushman, Dina  and
      Cohen, Kevin Bretonnel  and
      Ananiadou, Sophia  and
      Tsujii, Junichi",
    booktitle = "Proceedings of the {B}io{NLP} 2018 workshop",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W18-2302/",
    doi = "10.18653/v1/W18-2302",
    pages = "12--17",
    abstract = "We introduce a multi-task learning model for cause-of-death classification of verbal autopsy narratives that jointly learns to output interpretable key phrases. Adding these key phrases outperforms the baseline model and topic modeling features."
}

Download as File

<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="jeblee-etal-2018-multi">
    <titleInfo>
        <title>Multi-task learning for interpretable cause of death classification using key phrase prediction</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Serena</namePart>
        <namePart type="family">Jeblee</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Mireille</namePart>
        <namePart type="family">Gomes</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Graeme</namePart>
        <namePart type="family">Hirst</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2018-07</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the BioNLP 2018 workshop</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Dina</namePart>
            <namePart type="family">Demner-Fushman</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Kevin</namePart>
            <namePart type="given">Bretonnel</namePart>
            <namePart type="family">Cohen</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Sophia</namePart>
            <namePart type="family">Ananiadou</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Junichi</namePart>
            <namePart type="family">Tsujii</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Melbourne, Australia</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>We introduce a multi-task learning model for cause-of-death classification of verbal autopsy narratives that jointly learns to output interpretable key phrases. Adding these key phrases outperforms the baseline model and topic modeling features.</abstract>
    <identifier type="citekey">jeblee-etal-2018-multi</identifier>
    <identifier type="doi">10.18653/v1/W18-2302</identifier>
    <location>
        <url>https://aclanthology.org/W18-2302/</url>
    </location>
    <part>
        <date>2018-07</date>
        <extent unit="page">
            <start>12</start>
            <end>17</end>
        </extent>
    </part>
</mods>
</modsCollection>

Download as File

%0 Conference Proceedings
%T Multi-task learning for interpretable cause of death classification using key phrase prediction
%A Jeblee, Serena
%A Gomes, Mireille
%A Hirst, Graeme
%Y Demner-Fushman, Dina
%Y Cohen, Kevin Bretonnel
%Y Ananiadou, Sophia
%Y Tsujii, Junichi
%S Proceedings of the BioNLP 2018 workshop
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F jeblee-etal-2018-multi
%X We introduce a multi-task learning model for cause-of-death classification of verbal autopsy narratives that jointly learns to output interpretable key phrases. Adding these key phrases outperforms the baseline model and topic modeling features.
%R 10.18653/v1/W18-2302
%U https://aclanthology.org/W18-2302/
%U https://doi.org/10.18653/v1/W18-2302
%P 12-17

Download as File

Markdown (Informal)

[Multi-task learning for interpretable cause of death classification using key phrase prediction](https://aclanthology.org/W18-2302/) (Jeblee et al., BioNLP 2018)

Multi-task learning for interpretable cause of death classification using key phrase prediction (Jeblee et al., BioNLP 2018)

ACL

Serena Jeblee, Mireille Gomes, and Graeme Hirst. 2018. Multi-task learning for interpretable cause of death classification using key phrase prediction. In Proceedings of the BioNLP 2018 workshop, pages 12–17, Melbourne, Australia. Association for Computational Linguistics.