<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="holtzapple-etal-2021-context">
    <titleInfo>
        <title>Context-aware query design combines knowledge and data for efficient reading and reasoning</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Emilee</namePart>
        <namePart type="family">Holtzapple</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Brent</namePart>
        <namePart type="family">Cochran</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Natasa</namePart>
        <namePart type="family">Miskov-Zivanov</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2021-06</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 20th Workshop on Biomedical Language Processing</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">Online</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>The amount of biomedical literature has vastly increased over the past few decades. As a result, the sheer quantity of accessible information is overwhelming, and complicates manual information retrieval. Automated methods seek to speed up information retrieval from biomedical literature. However, such automated methods are still too time-intensive to survey all existing biomedical literature. We present a methodology for automatically generating literature queries that select relevant papers based on biological data. By using differentially expressed genes to inform our literature searches, we focus information extraction on mechanistic signaling details that are crucial for the disease or context of interest.</abstract>
    <identifier type="citekey">holtzapple-etal-2021-context</identifier>
    <identifier type="doi">10.18653/v1/2021.bionlp-1.26</identifier>
    <location>
        <url>https://aclanthology.org/2021.bionlp-1.26</url>
    </location>
    <part>
        <date>2021-06</date>
        <extent unit="page">
            <start>238</start>
            <end>246</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Context-aware query design combines knowledge and data for efficient reading and reasoning
%A Holtzapple, Emilee
%A Cochran, Brent
%A Miskov-Zivanov, Natasa
%Y Demner-Fushman, Dina
%Y Cohen, Kevin Bretonnel
%Y Ananiadou, Sophia
%Y Tsujii, Junichi
%S Proceedings of the 20th Workshop on Biomedical Language Processing
%D 2021
%8 June
%I Association for Computational Linguistics
%C Online
%F holtzapple-etal-2021-context
%X The amount of biomedical literature has vastly increased over the past few decades. As a result, the sheer quantity of accessible information is overwhelming, and complicates manual information retrieval. Automated methods seek to speed up information retrieval from biomedical literature. However, such automated methods are still too time-intensive to survey all existing biomedical literature. We present a methodology for automatically generating literature queries that select relevant papers based on biological data. By using differentially expressed genes to inform our literature searches, we focus information extraction on mechanistic signaling details that are crucial for the disease or context of interest.
%R 10.18653/v1/2021.bionlp-1.26
%U https://aclanthology.org/2021.bionlp-1.26
%U https://doi.org/10.18653/v1/2021.bionlp-1.26
%P 238-246

Markdown (Informal)

[Context-aware query design combines knowledge and data for efficient reading and reasoning](https://aclanthology.org/2021.bionlp-1.26) (Holtzapple et al., BioNLP 2021)

• Context-aware query design combines knowledge and data for efficient reading and reasoning (Holtzapple et al., BioNLP 2021)

ACL

• Emilee Holtzapple, Brent Cochran, and Natasa Miskov-Zivanov. 2021. Context-aware query design combines knowledge and data for efficient reading and reasoning. In Proceedings of the 20th Workshop on Biomedical Language Processing, pages 238–246, Online. Association for Computational Linguistics.