<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="zhao-etal-2018-framework">
    <titleInfo>
        <title>A Framework for Developing and Evaluating Word Embeddings of Drug-named Entity</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Mengnan</namePart>
        <namePart type="family">Zhao</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Aaron</namePart>
        <namePart type="given">J</namePart>
        <namePart type="family">Masino</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Christopher</namePart>
        <namePart type="given">C</namePart>
        <namePart type="family">Yang</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 investigate the quality of task specific word embeddings created with relatively small, targeted corpora. We present a comprehensive evaluation framework including both intrinsic and extrinsic evaluation that can be expanded to named entities beyond drug name. Intrinsic evaluation results tell that drug name embeddings created with a domain specific document corpus outperformed the previously published versions that derived from a very large general text corpus. Extrinsic evaluation uses word embedding for the task of drug name recognition with Bi-LSTM model and the results demonstrate the advantage of using domain-specific word embeddings as the only input feature for drug name recognition with F1-score achieving 0.91. This work suggests that it may be advantageous to derive domain specific embeddings for certain tasks even when the domain specific corpus is of limited size.</abstract>
    <identifier type="citekey">zhao-etal-2018-framework</identifier>
    <identifier type="doi">10.18653/v1/W18-2319</identifier>
    <location>
        <url>https://aclanthology.org/W18-2319</url>
    </location>
    <part>
        <date>2018-07</date>
        <extent unit="page">
            <start>156</start>
            <end>160</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T A Framework for Developing and Evaluating Word Embeddings of Drug-named Entity
%A Zhao, Mengnan
%A Masino, Aaron J.
%A Yang, Christopher C.
%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 zhao-etal-2018-framework
%X We investigate the quality of task specific word embeddings created with relatively small, targeted corpora. We present a comprehensive evaluation framework including both intrinsic and extrinsic evaluation that can be expanded to named entities beyond drug name. Intrinsic evaluation results tell that drug name embeddings created with a domain specific document corpus outperformed the previously published versions that derived from a very large general text corpus. Extrinsic evaluation uses word embedding for the task of drug name recognition with Bi-LSTM model and the results demonstrate the advantage of using domain-specific word embeddings as the only input feature for drug name recognition with F1-score achieving 0.91. This work suggests that it may be advantageous to derive domain specific embeddings for certain tasks even when the domain specific corpus is of limited size.
%R 10.18653/v1/W18-2319
%U https://aclanthology.org/W18-2319
%U https://doi.org/10.18653/v1/W18-2319
%P 156-160

Markdown (Informal)

[A Framework for Developing and Evaluating Word Embeddings of Drug-named Entity](https://aclanthology.org/W18-2319) (Zhao et al., BioNLP 2018)

• A Framework for Developing and Evaluating Word Embeddings of Drug-named Entity (Zhao et al., BioNLP 2018)

ACL

• Mengnan Zhao, Aaron J. Masino, and Christopher C. Yang. 2018. A Framework for Developing and Evaluating Word Embeddings of Drug-named Entity. In Proceedings of the BioNLP 2018 workshop, pages 156–160, Melbourne, Australia. Association for Computational Linguistics.