<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="schwarzenberg-etal-2019-neural">
    <titleInfo>
        <title>Neural Vector Conceptualization for Word Vector Space Interpretation</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Robert</namePart>
        <namePart type="family">Schwarzenberg</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Lisa</namePart>
        <namePart type="family">Raithel</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">David</namePart>
        <namePart type="family">Harbecke</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2019-06</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 3rd Workshop on Evaluating Vector Space Representations for NLP</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Anna</namePart>
            <namePart type="family">Rogers</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Aleksandr</namePart>
            <namePart type="family">Drozd</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Anna</namePart>
            <namePart type="family">Rumshisky</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Yoav</namePart>
            <namePart type="family">Goldberg</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Minneapolis, USA</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Distributed word vector spaces are considered hard to interpret which hinders the understanding of natural language processing (NLP) models. In this work, we introduce a new method to interpret arbitrary samples from a word vector space. To this end, we train a neural model to conceptualize word vectors, which means that it activates higher order concepts it recognizes in a given vector. Contrary to prior approaches, our model operates in the original vector space and is capable of learning non-linear relations between word vectors and concepts. Furthermore, we show that it produces considerably less entropic concept activation profiles than the popular cosine similarity.</abstract>
    <identifier type="citekey">schwarzenberg-etal-2019-neural</identifier>
    <identifier type="doi">10.18653/v1/W19-2001</identifier>
    <location>
        <url>https://aclanthology.org/W19-2001</url>
    </location>
    <part>
        <date>2019-06</date>
        <extent unit="page">
            <start>1</start>
            <end>7</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Neural Vector Conceptualization for Word Vector Space Interpretation
%A Schwarzenberg, Robert
%A Raithel, Lisa
%A Harbecke, David
%Y Rogers, Anna
%Y Drozd, Aleksandr
%Y Rumshisky, Anna
%Y Goldberg, Yoav
%S Proceedings of the 3rd Workshop on Evaluating Vector Space Representations for NLP
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, USA
%F schwarzenberg-etal-2019-neural
%X Distributed word vector spaces are considered hard to interpret which hinders the understanding of natural language processing (NLP) models. In this work, we introduce a new method to interpret arbitrary samples from a word vector space. To this end, we train a neural model to conceptualize word vectors, which means that it activates higher order concepts it recognizes in a given vector. Contrary to prior approaches, our model operates in the original vector space and is capable of learning non-linear relations between word vectors and concepts. Furthermore, we show that it produces considerably less entropic concept activation profiles than the popular cosine similarity.
%R 10.18653/v1/W19-2001
%U https://aclanthology.org/W19-2001
%U https://doi.org/10.18653/v1/W19-2001
%P 1-7

Markdown (Informal)

[Neural Vector Conceptualization for Word Vector Space Interpretation](https://aclanthology.org/W19-2001) (Schwarzenberg et al., RepEval 2019)

• Neural Vector Conceptualization for Word Vector Space Interpretation (Schwarzenberg et al., RepEval 2019)

ACL

• Robert Schwarzenberg, Lisa Raithel, and David Harbecke. 2019. Neural Vector Conceptualization for Word Vector Space Interpretation. In Proceedings of the 3rd Workshop on Evaluating Vector Space Representations for NLP, pages 1–7, Minneapolis, USA. Association for Computational Linguistics.