@inproceedings{hoyle-etal-2019-combining,
title = "{C}ombining {S}entiment {L}exica with a {M}ulti-{V}iew {V}ariational {A}utoencoder",
author = "Hoyle, Alexander Miserlis and
Wolf-Sonkin, Lawrence and
Wallach, Hanna and
Cotterell, Ryan and
Augenstein, Isabelle",
editor = "Burstein, Jill and
Doran, Christy and
Solorio, Thamar",
booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N19-1065",
doi = "10.18653/v1/N19-1065",
pages = "635--640",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Combining Sentiment Lexica with a Multi-View Variational Autoencoder
%A Hoyle, Alexander Miserlis
%A Wolf-Sonkin, Lawrence
%A Wallach, Hanna
%A Cotterell, Ryan
%A Augenstein, Isabelle
%Y Burstein, Jill
%Y Doran, Christy
%Y Solorio, Thamar
%S 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)
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F hoyle-etal-2019-combining
%X 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.
%R 10.18653/v1/N19-1065
%U https://aclanthology.org/N19-1065
%U https://doi.org/10.18653/v1/N19-1065
%P 635-640
Markdown (Informal)
[Combining Sentiment Lexica with a Multi-View Variational Autoencoder](https://aclanthology.org/N19-1065) (Hoyle et al., NAACL 2019)
ACL
- Alexander Miserlis Hoyle, Lawrence Wolf-Sonkin, Hanna Wallach, Ryan Cotterell, and Isabelle Augenstein. 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), pages 635–640, Minneapolis, Minnesota. Association for Computational Linguistics.