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        <title>Labeled Anchors and a Scalable, Transparent, and Interactive Classifier</title>
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        <namePart type="given">Jeffrey</namePart>
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    <name type="personal">
        <namePart type="given">Stephen</namePart>
        <namePart type="family">Cowley</namePart>
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    <name type="personal">
        <namePart type="given">Wilson</namePart>
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    <name type="personal">
        <namePart type="given">Emily</namePart>
        <namePart type="family">Hales</namePart>
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            <roleTerm authority="marcrelator" type="text">author</roleTerm>
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    <name type="personal">
        <namePart type="given">Kevin</namePart>
        <namePart type="family">Seppi</namePart>
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            <roleTerm authority="marcrelator" type="text">author</roleTerm>
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            <title>Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing</title>
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            <namePart type="given">Ellen</namePart>
            <namePart type="family">Riloff</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">David</namePart>
            <namePart type="family">Chiang</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Julia</namePart>
            <namePart type="family">Hockenmaier</namePart>
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            <namePart type="given">Jun’ichi</namePart>
            <namePart type="family">Tsujii</namePart>
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    <abstract>We propose Labeled Anchors, an interactive and supervised topic model based on the anchor words algorithm (Arora et al., 2013). Labeled Anchors is similar to Supervised Anchors (Nguyen et al., 2014) in that it extends the vector-space representation of words to include document labels. However, our formulation also admits a classifier which requires no training beyond inferring topics, which means our approach is also fast enough to be interactive. We run a small user study that demonstrates that untrained users can interactively update topics in order to improve classification accuracy.</abstract>
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        <date>2018-oct-nov</date>
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%0 Conference Proceedings
%T Labeled Anchors and a Scalable, Transparent, and Interactive Classifier
%A Lund, Jeffrey
%A Cowley, Stephen
%A Fearn, Wilson
%A Hales, Emily
%A Seppi, Kevin
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F lund-etal-2018-labeled
%X We propose Labeled Anchors, an interactive and supervised topic model based on the anchor words algorithm (Arora et al., 2013). Labeled Anchors is similar to Supervised Anchors (Nguyen et al., 2014) in that it extends the vector-space representation of words to include document labels. However, our formulation also admits a classifier which requires no training beyond inferring topics, which means our approach is also fast enough to be interactive. We run a small user study that demonstrates that untrained users can interactively update topics in order to improve classification accuracy.
%R 10.18653/v1/D18-1095
%U https://aclanthology.org/D18-1095
%U https://doi.org/10.18653/v1/D18-1095
%P 824-829

Markdown (Informal)

[Labeled Anchors and a Scalable, Transparent, and Interactive Classifier](https://aclanthology.org/D18-1095) (Lund et al., EMNLP 2018)

• Labeled Anchors and a Scalable, Transparent, and Interactive Classifier (Lund et al., EMNLP 2018)

ACL

• Jeffrey Lund, Stephen Cowley, Wilson Fearn, Emily Hales, and Kevin Seppi. 2018. Labeled Anchors and a Scalable, Transparent, and Interactive Classifier. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 824–829, Brussels, Belgium. Association for Computational Linguistics.