<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="ngo-etal-2019-neural">
    <titleInfo>
        <title>Neural Dependency Parsing of Biomedical Text: TurkuNLP entry in the CRAFT Structural Annotation Task</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Thang</namePart>
        <namePart type="given">Minh</namePart>
        <namePart type="family">Ngo</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Jenna</namePart>
        <namePart type="family">Kanerva</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Filip</namePart>
        <namePart type="family">Ginter</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Sampo</namePart>
        <namePart type="family">Pyysalo</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2019-11</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 5th Workshop on BioNLP Open Shared Tasks</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Kim</namePart>
            <namePart type="family">Jin-Dong</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Nédellec</namePart>
            <namePart type="family">Claire</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Bossy</namePart>
            <namePart type="family">Robert</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Deléger</namePart>
            <namePart type="family">Louise</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Hong Kong, China</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>We present the approach taken by the TurkuNLP group in the CRAFT Structural Annotation task, a shared task on dependency parsing. Our approach builds primarily on the Turku neural parser, a native dependency parser that ranked among the best in the recent CoNLL tasks on parsing Universal Dependencies. To adapt the parser to the biomedical domain, we considered and evaluated a number of approaches, including the generation of custom word embeddings, combination with other in-domain resources, and the incorporation of information from named entity recognition. We achieved a labeled attachment score of 89.7%, the best result among task participants.</abstract>
    <identifier type="citekey">ngo-etal-2019-neural</identifier>
    <identifier type="doi">10.18653/v1/D19-5728</identifier>
    <location>
        <url>https://aclanthology.org/D19-5728</url>
    </location>
    <part>
        <date>2019-11</date>
        <extent unit="page">
            <start>206</start>
            <end>215</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Neural Dependency Parsing of Biomedical Text: TurkuNLP entry in the CRAFT Structural Annotation Task
%A Ngo, Thang Minh
%A Kanerva, Jenna
%A Ginter, Filip
%A Pyysalo, Sampo
%Y Jin-Dong, Kim
%Y Claire, Nédellec
%Y Robert, Bossy
%Y Louise, Deléger
%S Proceedings of the 5th Workshop on BioNLP Open Shared Tasks
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F ngo-etal-2019-neural
%X We present the approach taken by the TurkuNLP group in the CRAFT Structural Annotation task, a shared task on dependency parsing. Our approach builds primarily on the Turku neural parser, a native dependency parser that ranked among the best in the recent CoNLL tasks on parsing Universal Dependencies. To adapt the parser to the biomedical domain, we considered and evaluated a number of approaches, including the generation of custom word embeddings, combination with other in-domain resources, and the incorporation of information from named entity recognition. We achieved a labeled attachment score of 89.7%, the best result among task participants.
%R 10.18653/v1/D19-5728
%U https://aclanthology.org/D19-5728
%U https://doi.org/10.18653/v1/D19-5728
%P 206-215

Markdown (Informal)

[Neural Dependency Parsing of Biomedical Text: TurkuNLP entry in the CRAFT Structural Annotation Task](https://aclanthology.org/D19-5728) (Ngo et al., BioNLP 2019)

• Neural Dependency Parsing of Biomedical Text: TurkuNLP entry in the CRAFT Structural Annotation Task (Ngo et al., BioNLP 2019)

ACL

• Thang Minh Ngo, Jenna Kanerva, Filip Ginter, and Sampo Pyysalo. 2019. Neural Dependency Parsing of Biomedical Text: TurkuNLP entry in the CRAFT Structural Annotation Task. In Proceedings of the 5th Workshop on BioNLP Open Shared Tasks, pages 206–215, Hong Kong, China. Association for Computational Linguistics.