<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="van-cranenburgh-2018-active">
    <titleInfo>
        <title>Active DOP: A constituency treebank annotation tool with online learning</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Andreas</namePart>
        <namePart type="family">van Cranenburgh</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2018-08</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Dongyan</namePart>
            <namePart type="family">Zhao</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Santa Fe, New Mexico</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>We present a language-independent treebank annotation tool supporting rich annotations with discontinuous constituents and function tags. Candidate analyses are generated by an exemplar-based parsing model that immediately learns from each new annotated sentence during annotation. This makes it suitable for situations in which only a limited seed treebank is available, or a radically different domain is being annotated. The tool offers the possibility to experiment with and evaluate active learning methods to speed up annotation in a naturalistic setting, i.e., measuring actual annotation costs and tracking specific user interactions. The code is made available under the GNU GPL license at https://github.com/andreasvc/activedop.</abstract>
    <identifier type="citekey">van-cranenburgh-2018-active</identifier>
    <location>
        <url>https://aclanthology.org/C18-2009</url>
    </location>
    <part>
        <date>2018-08</date>
        <extent unit="page">
            <start>38</start>
            <end>42</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Active DOP: A constituency treebank annotation tool with online learning
%A van Cranenburgh, Andreas
%Y Zhao, Dongyan
%S Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico
%F van-cranenburgh-2018-active
%X We present a language-independent treebank annotation tool supporting rich annotations with discontinuous constituents and function tags. Candidate analyses are generated by an exemplar-based parsing model that immediately learns from each new annotated sentence during annotation. This makes it suitable for situations in which only a limited seed treebank is available, or a radically different domain is being annotated. The tool offers the possibility to experiment with and evaluate active learning methods to speed up annotation in a naturalistic setting, i.e., measuring actual annotation costs and tracking specific user interactions. The code is made available under the GNU GPL license at https://github.com/andreasvc/activedop.
%U https://aclanthology.org/C18-2009
%P 38-42

Markdown (Informal)

[Active DOP: A constituency treebank annotation tool with online learning](https://aclanthology.org/C18-2009) (van Cranenburgh, COLING 2018)

• Active DOP: A constituency treebank annotation tool with online learning (van Cranenburgh, COLING 2018)

ACL

• Andreas van Cranenburgh. 2018. Active DOP: A constituency treebank annotation tool with online learning. In Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations, pages 38–42, Santa Fe, New Mexico. Association for Computational Linguistics.