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    <titleInfo>
        <title>A comparison of Named-Entity Disambiguation and Word Sense Disambiguation</title>
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    <name type="personal">
        <namePart type="given">Angel</namePart>
        <namePart type="family">Chang</namePart>
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    <name type="personal">
        <namePart type="given">Valentin</namePart>
        <namePart type="given">I</namePart>
        <namePart type="family">Spitkovsky</namePart>
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    <name type="personal">
        <namePart type="given">Christopher</namePart>
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        <namePart type="family">Manning</namePart>
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    <name type="personal">
        <namePart type="given">Eneko</namePart>
        <namePart type="family">Agirre</namePart>
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            <roleTerm authority="marcrelator" type="text">author</roleTerm>
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        <dateIssued>2016-may</dateIssued>
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            <title>Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC’16)</title>
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            <publisher>European Language Resources Association (ELRA)</publisher>
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                <placeTerm type="text">Portorož, Slovenia</placeTerm>
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    <abstract>Named Entity Disambiguation (NED) is the task of linking a named-entity mention to an instance in a knowledge-base, typically Wikipedia-derived resources like DBpedia. This task is closely related to word-sense disambiguation (WSD), where the mention of an open-class word is linked to a concept in a knowledge-base, typically WordNet. This paper analyzes the relation between two annotated datasets on NED and WSD, highlighting the commonalities and differences. We detail the methods to construct a NED system following the WSD word-expert approach, where we need a dictionary and one classifier is built for each target entity mention string. Constructing a dictionary for NED proved challenging, and although similarity and ambiguity are higher for NED, the results are also higher due to the larger number of training data, and the more crisp and skewed meaning differences.</abstract>
    <identifier type="citekey">chang-etal-2016-comparison</identifier>
    <location>
        <url>https://aclanthology.org/L16-1139</url>
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        <date>2016-may</date>
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            <start>860</start>
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%0 Conference Proceedings
%T A comparison of Named-Entity Disambiguation and Word Sense Disambiguation
%A Chang, Angel
%A Spitkovsky, Valentin I.
%A Manning, Christopher D.
%A Agirre, Eneko
%S Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC’16)
%D 2016
%8 may
%I European Language Resources Association (ELRA)
%C Portorož, Slovenia
%F chang-etal-2016-comparison
%X Named Entity Disambiguation (NED) is the task of linking a named-entity mention to an instance in a knowledge-base, typically Wikipedia-derived resources like DBpedia. This task is closely related to word-sense disambiguation (WSD), where the mention of an open-class word is linked to a concept in a knowledge-base, typically WordNet. This paper analyzes the relation between two annotated datasets on NED and WSD, highlighting the commonalities and differences. We detail the methods to construct a NED system following the WSD word-expert approach, where we need a dictionary and one classifier is built for each target entity mention string. Constructing a dictionary for NED proved challenging, and although similarity and ambiguity are higher for NED, the results are also higher due to the larger number of training data, and the more crisp and skewed meaning differences.
%U https://aclanthology.org/L16-1139
%P 860-867

Markdown (Informal)

[A comparison of Named-Entity Disambiguation and Word Sense Disambiguation](https://aclanthology.org/L16-1139) (Chang et al., LREC 2016)

• A comparison of Named-Entity Disambiguation and Word Sense Disambiguation (Chang et al., LREC 2016)

ACL

• Angel Chang, Valentin I. Spitkovsky, Christopher D. Manning, and Eneko Agirre. 2016. A comparison of Named-Entity Disambiguation and Word Sense Disambiguation. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16), pages 860–867, Portorož, Slovenia. European Language Resources Association (ELRA).