Standard
Combining Sentiment Lexica with a Multi-View Variational Autoencoder. / Hoyle, Alexander Miserlis; Wolf-sonkin, Lawrence; Wallach, Hanna; Cotterell, Ryan; Augenstein, Isabelle.
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). Association for Computational Linguistics, 2019. p. 635-640.
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Harvard
Hoyle, AM, Wolf-sonkin, L, Wallach, H, Cotterell, R
& Augenstein, I 2019,
Combining Sentiment Lexica with a Multi-View Variational Autoencoder. in
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). Association for Computational Linguistics, pp. 635-640, 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - NAACL-HLT 2019, Minneapolis, United States,
03/06/2019.
https://doi.org/10.18653/v1/N19-1065APA
Hoyle, A. M., Wolf-sonkin, L., Wallach, H., Cotterell, R.
, & Augenstein, I. (2019).
Combining Sentiment Lexica with a Multi-View Variational Autoencoder. In
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) (pp. 635-640). Association for Computational Linguistics.
https://doi.org/10.18653/v1/N19-1065Vancouver
Hoyle AM, Wolf-sonkin L, Wallach H, Cotterell R
, Augenstein I.
Combining Sentiment Lexica with a Multi-View Variational Autoencoder. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). Association for Computational Linguistics. 2019. p. 635-640
https://doi.org/10.18653/v1/N19-1065Author
Hoyle, Alexander Miserlis ; Wolf-sonkin, Lawrence ; Wallach, Hanna ; Cotterell, Ryan ; Augenstein, Isabelle. / Combining Sentiment Lexica with a Multi-View Variational Autoencoder. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). Association for Computational Linguistics, 2019. pp. 635-640
Bibtex
@inproceedings{908a0dbc3c854d18a8a7a08b6d256453,
title = "Combining Sentiment Lexica with a Multi-View Variational Autoencoder",
abstract = "When assigning quantitative labels to a dataset, different methodologies may rely on different scales. In particular, when assigning polarities to words in a sentiment lexicon, annotators may use binary, categorical, or continuous labels. Naturally, it is of interest to unify these labels from disparate scales to both achieve maximal coverage over words and to create a single, more robust sentiment lexicon while retaining scale coherence. We introduce a generative model of sentiment lexica to combine disparate scales into a common latent representation. We realize this model with a novel multi-view variational autoencoder (VAE), called SentiVAE. We evaluate our approach via a downstream text classification task involving nine English-Language sentiment analysis datasets; our representation outperforms six individual sentiment lexica, as well as a straightforward combination thereof.",
author = "Hoyle, {Alexander Miserlis} and Lawrence Wolf-sonkin and Hanna Wallach and Ryan Cotterell and Isabelle Augenstein",
year = "2019",
doi = "10.18653/v1/N19-1065",
language = "English",
pages = "635--640",
booktitle = "Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
publisher = "Association for Computational Linguistics",
note = "2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - NAACL-HLT 2019 ; Conference date: 03-06-2019 Through 07-06-2019",
}
RIS
TY - GEN
T1 - Combining Sentiment Lexica with a Multi-View Variational Autoencoder
AU - Hoyle, Alexander Miserlis
AU - Wolf-sonkin, Lawrence
AU - Wallach, Hanna
AU - Cotterell, Ryan
AU - Augenstein, Isabelle
PY - 2019
Y1 - 2019
N2 - When assigning quantitative labels to a dataset, different methodologies may rely on different scales. In particular, when assigning polarities to words in a sentiment lexicon, annotators may use binary, categorical, or continuous labels. Naturally, it is of interest to unify these labels from disparate scales to both achieve maximal coverage over words and to create a single, more robust sentiment lexicon while retaining scale coherence. We introduce a generative model of sentiment lexica to combine disparate scales into a common latent representation. We realize this model with a novel multi-view variational autoencoder (VAE), called SentiVAE. We evaluate our approach via a downstream text classification task involving nine English-Language sentiment analysis datasets; our representation outperforms six individual sentiment lexica, as well as a straightforward combination thereof.
AB - When assigning quantitative labels to a dataset, different methodologies may rely on different scales. In particular, when assigning polarities to words in a sentiment lexicon, annotators may use binary, categorical, or continuous labels. Naturally, it is of interest to unify these labels from disparate scales to both achieve maximal coverage over words and to create a single, more robust sentiment lexicon while retaining scale coherence. We introduce a generative model of sentiment lexica to combine disparate scales into a common latent representation. We realize this model with a novel multi-view variational autoencoder (VAE), called SentiVAE. We evaluate our approach via a downstream text classification task involving nine English-Language sentiment analysis datasets; our representation outperforms six individual sentiment lexica, as well as a straightforward combination thereof.
U2 - 10.18653/v1/N19-1065
DO - 10.18653/v1/N19-1065
M3 - Article in proceedings
SP - 635
EP - 640
BT - Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
PB - Association for Computational Linguistics
T2 - 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - NAACL-HLT 2019
Y2 - 3 June 2019 through 7 June 2019
ER -
