<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="durrett-klein-2014-joint">
    <titleInfo>
        <title>A Joint Model for Entity Analysis: Coreference, Typing, and Linking</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Greg</namePart>
        <namePart type="family">Durrett</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Dan</namePart>
        <namePart type="family">Klein</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2014</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <genre authority="bibutilsgt">journal article</genre>
    <relatedItem type="host">
        <titleInfo>
            <title>Transactions of the Association for Computational Linguistics</title>
        </titleInfo>
        <originInfo>
            <issuance>continuing</issuance>
            <publisher>MIT Press</publisher>
            <place>
                <placeTerm type="text">Cambridge, MA</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">periodical</genre>
        <genre authority="bibutilsgt">academic journal</genre>
    </relatedItem>
    <abstract>We present a joint model of three core tasks in the entity analysis stack: coreference resolution (within-document clustering), named entity recognition (coarse semantic typing), and entity linking (matching to Wikipedia entities). Our model is formally a structured conditional random field. Unary factors encode local features from strong baselines for each task. We then add binary and ternary factors to capture cross-task interactions, such as the constraint that coreferent mentions have the same semantic type. On the ACE 2005 and OntoNotes datasets, we achieve state-of-the-art results for all three tasks. Moreover, joint modeling improves performance on each task over strong independent baselines.</abstract>
    <identifier type="citekey">durrett-klein-2014-joint</identifier>
    <identifier type="doi">10.1162/tacl_a_00197</identifier>
    <location>
        <url>https://aclanthology.org/Q14-1037/</url>
    </location>
    <part>
        <date>2014</date>
        <detail type="volume"><number>2</number></detail>
        <extent unit="page">
            <start>477</start>
            <end>490</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Journal Article
%T A Joint Model for Entity Analysis: Coreference, Typing, and Linking
%A Durrett, Greg
%A Klein, Dan
%J Transactions of the Association for Computational Linguistics
%D 2014
%V 2
%I MIT Press
%C Cambridge, MA
%F durrett-klein-2014-joint
%X We present a joint model of three core tasks in the entity analysis stack: coreference resolution (within-document clustering), named entity recognition (coarse semantic typing), and entity linking (matching to Wikipedia entities). Our model is formally a structured conditional random field. Unary factors encode local features from strong baselines for each task. We then add binary and ternary factors to capture cross-task interactions, such as the constraint that coreferent mentions have the same semantic type. On the ACE 2005 and OntoNotes datasets, we achieve state-of-the-art results for all three tasks. Moreover, joint modeling improves performance on each task over strong independent baselines.
%R 10.1162/tacl_a_00197
%U https://aclanthology.org/Q14-1037/
%U https://doi.org/10.1162/tacl_a_00197
%P 477-490

Markdown (Informal)

[A Joint Model for Entity Analysis: Coreference, Typing, and Linking](https://aclanthology.org/Q14-1037/) (Durrett & Klein, TACL 2014)

• A Joint Model for Entity Analysis: Coreference, Typing, and Linking (Durrett & Klein, TACL 2014)

ACL

• Greg Durrett and Dan Klein. 2014. A Joint Model for Entity Analysis: Coreference, Typing, and Linking. Transactions of the Association for Computational Linguistics, 2:477–490.