Labeled Anchors and a Scalable, Transparent, and Interactive Classifier

Jeffrey Lund; Stephen Cowley; Wilson Fearn; Emily Hales; Kevin Seppi

doi:10.18653/v1/D18-1095

Labeled Anchors and a Scalable, Transparent, and Interactive Classifier

Jeffrey Lund, Stephen Cowley, Wilson Fearn, Emily Hales, Kevin Seppi

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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.

Anthology ID:: D18-1095
Volume:: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Month:: October-November
Year:: 2018
Address:: Brussels, Belgium
Editors:: Ellen Riloff, David Chiang, Julia Hockenmaier, Jun’ichi Tsujii
Venue:: EMNLP
SIG:: SIGDAT
Publisher:: Association for Computational Linguistics
Note:
Pages:: 824–829
Language:
URL:: https://aclanthology.org/D18-1095/
DOI:: 10.18653/v1/D18-1095
Bibkey:
Cite (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.
Cite (Informal):: Labeled Anchors and a Scalable, Transparent, and Interactive Classifier (Lund et al., EMNLP 2018)
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PDF:: https://aclanthology.org/D18-1095.pdf

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@inproceedings{lund-etal-2018-labeled,
    title = "Labeled Anchors and a Scalable, Transparent, and Interactive Classifier",
    author = "Lund, Jeffrey  and
      Cowley, Stephen  and
      Fearn, Wilson  and
      Hales, Emily  and
      Seppi, Kevin",
    editor = "Riloff, Ellen  and
      Chiang, David  and
      Hockenmaier, Julia  and
      Tsujii, Jun{'}ichi",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/D18-1095/",
    doi = "10.18653/v1/D18-1095",
    pages = "824--829",
    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."
}

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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

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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.