<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="gupta-etal-2017-distributed">
    <titleInfo>
        <title>Distributed Prediction of Relations for Entities: The Easy, The Difficult, and The Impossible</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Abhijeet</namePart>
        <namePart type="family">Gupta</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Gemma</namePart>
        <namePart type="family">Boleda</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Sebastian</namePart>
        <namePart type="family">Padó</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2017-08</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*SEM 2017)</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Nancy</namePart>
            <namePart type="family">Ide</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Aurélie</namePart>
            <namePart type="family">Herbelot</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Lluís</namePart>
            <namePart type="family">Màrquez</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Vancouver, Canada</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Word embeddings are supposed to provide easy access to semantic relations such as “male of” (man–woman). While this claim has been investigated for concepts, little is known about the distributional behavior of relations of (Named) Entities. We describe two word embedding-based models that predict values for relational attributes of entities, and analyse them. The task is challenging, with major performance differences between relations. Contrary to many NLP tasks, high difficulty for a relation does not result from low frequency, but from (a) one-to-many mappings; and (b) lack of context patterns expressing the relation that are easy to pick up by word embeddings.</abstract>
    <identifier type="citekey">gupta-etal-2017-distributed</identifier>
    <identifier type="doi">10.18653/v1/S17-1012</identifier>
    <location>
        <url>https://aclanthology.org/S17-1012</url>
    </location>
    <part>
        <date>2017-08</date>
        <extent unit="page">
            <start>104</start>
            <end>109</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Distributed Prediction of Relations for Entities: The Easy, The Difficult, and The Impossible
%A Gupta, Abhijeet
%A Boleda, Gemma
%A Padó, Sebastian
%Y Ide, Nancy
%Y Herbelot, Aurélie
%Y Màrquez, Lluís
%S Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*SEM 2017)
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, Canada
%F gupta-etal-2017-distributed
%X Word embeddings are supposed to provide easy access to semantic relations such as “male of” (man–woman). While this claim has been investigated for concepts, little is known about the distributional behavior of relations of (Named) Entities. We describe two word embedding-based models that predict values for relational attributes of entities, and analyse them. The task is challenging, with major performance differences between relations. Contrary to many NLP tasks, high difficulty for a relation does not result from low frequency, but from (a) one-to-many mappings; and (b) lack of context patterns expressing the relation that are easy to pick up by word embeddings.
%R 10.18653/v1/S17-1012
%U https://aclanthology.org/S17-1012
%U https://doi.org/10.18653/v1/S17-1012
%P 104-109

Markdown (Informal)

[Distributed Prediction of Relations for Entities: The Easy, The Difficult, and The Impossible](https://aclanthology.org/S17-1012) (Gupta et al., *SEM 2017)

• Distributed Prediction of Relations for Entities: The Easy, The Difficult, and The Impossible (Gupta et al., *SEM 2017)

ACL

• Abhijeet Gupta, Gemma Boleda, and Sebastian Padó. 2017. Distributed Prediction of Relations for Entities: The Easy, The Difficult, and The Impossible. In Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*SEM 2017), pages 104–109, Vancouver, Canada. Association for Computational Linguistics.