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    <titleInfo>
        <title>Design Challenges for Entity Linking</title>
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    <name type="personal">
        <namePart type="given">Xiao</namePart>
        <namePart type="family">Ling</namePart>
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    <name type="personal">
        <namePart type="given">Sameer</namePart>
        <namePart type="family">Singh</namePart>
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    <abstract>Recent research on entity linking (EL) has introduced a plethora of promising techniques, ranging from deep neural networks to joint inference. But despite numerous papers there is surprisingly little understanding of the state of the art in EL. We attack this confusion by analyzing differences between several versions of the EL problem and presenting a simple yet effective, modular, unsupervised system, called Vinculum, for entity linking. We conduct an extensive evaluation on nine data sets, comparing Vinculum with two state-of-the-art systems, and elucidate key aspects of the system that include mention extraction, candidate generation, entity type prediction, entity coreference, and coherence.</abstract>
    <identifier type="citekey">ling-etal-2015-design</identifier>
    <identifier type="doi">10.1162/tacl_a_00141</identifier>
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        <url>https://aclanthology.org/Q15-1023</url>
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        <date>2015</date>
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%0 Journal Article
%T Design Challenges for Entity Linking
%A Ling, Xiao
%A Singh, Sameer
%A Weld, Daniel S.
%J Transactions of the Association for Computational Linguistics
%D 2015
%V 3
%I MIT Press
%C Cambridge, MA
%F ling-etal-2015-design
%X Recent research on entity linking (EL) has introduced a plethora of promising techniques, ranging from deep neural networks to joint inference. But despite numerous papers there is surprisingly little understanding of the state of the art in EL. We attack this confusion by analyzing differences between several versions of the EL problem and presenting a simple yet effective, modular, unsupervised system, called Vinculum, for entity linking. We conduct an extensive evaluation on nine data sets, comparing Vinculum with two state-of-the-art systems, and elucidate key aspects of the system that include mention extraction, candidate generation, entity type prediction, entity coreference, and coherence.
%R 10.1162/tacl_a_00141
%U https://aclanthology.org/Q15-1023
%U https://doi.org/10.1162/tacl_a_00141
%P 315-328

Markdown (Informal)

[Design Challenges for Entity Linking](https://aclanthology.org/Q15-1023) (Ling et al., TACL 2015)

• Design Challenges for Entity Linking (Ling et al., TACL 2015)

ACL

• Xiao Ling, Sameer Singh, and Daniel S. Weld. 2015. Design Challenges for Entity Linking. Transactions of the Association for Computational Linguistics, 3:315–328.