<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="fang-chang-2014-entity">
    <titleInfo>
        <title>Entity Linking on Microblogs with Spatial and Temporal Signals</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Yuan</namePart>
        <namePart type="family">Fang</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Ming-Wei</namePart>
        <namePart type="family">Chang</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>Microblogs present an excellent opportunity for monitoring and analyzing world happenings. Given that words are often ambiguous, entity linking becomes a crucial step towards understanding microblogs. In this paper, we re-examine the problem of entity linking on microblogs. We first observe that spatiotemporal (i.e., spatial and temporal) signals play a key role, but they are not utilized in existing approaches. Thus, we propose a novel entity linking framework that incorporates spatiotemporal signals through a weakly supervised process. Using entity annotations on real-world data, our experiments show that the spatiotemporal model improves F1 by more than 10 points over existing systems. Finally, we present a qualitative study to visualize the effectiveness of our approach.</abstract>
    <identifier type="citekey">fang-chang-2014-entity</identifier>
    <identifier type="doi">10.1162/tacl_a_00181</identifier>
    <location>
        <url>https://aclanthology.org/Q14-1021</url>
    </location>
    <part>
        <date>2014</date>
        <detail type="volume"><number>2</number></detail>
        <extent unit="page">
            <start>259</start>
            <end>272</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Journal Article
%T Entity Linking on Microblogs with Spatial and Temporal Signals
%A Fang, Yuan
%A Chang, Ming-Wei
%J Transactions of the Association for Computational Linguistics
%D 2014
%V 2
%I MIT Press
%C Cambridge, MA
%F fang-chang-2014-entity
%X Microblogs present an excellent opportunity for monitoring and analyzing world happenings. Given that words are often ambiguous, entity linking becomes a crucial step towards understanding microblogs. In this paper, we re-examine the problem of entity linking on microblogs. We first observe that spatiotemporal (i.e., spatial and temporal) signals play a key role, but they are not utilized in existing approaches. Thus, we propose a novel entity linking framework that incorporates spatiotemporal signals through a weakly supervised process. Using entity annotations on real-world data, our experiments show that the spatiotemporal model improves F1 by more than 10 points over existing systems. Finally, we present a qualitative study to visualize the effectiveness of our approach.
%R 10.1162/tacl_a_00181
%U https://aclanthology.org/Q14-1021
%U https://doi.org/10.1162/tacl_a_00181
%P 259-272

Markdown (Informal)

[Entity Linking on Microblogs with Spatial and Temporal Signals](https://aclanthology.org/Q14-1021) (Fang & Chang, TACL 2014)

• Entity Linking on Microblogs with Spatial and Temporal Signals (Fang & Chang, TACL 2014)

ACL

• Yuan Fang and Ming-Wei Chang. 2014. Entity Linking on Microblogs with Spatial and Temporal Signals. Transactions of the Association for Computational Linguistics, 2:259–272.