@inproceedings{zhang-singh-2018-limbic,
title = "{L}imbic: Author-Based Sentiment Aspect Modeling Regularized with Word Embeddings and Discourse Relations",
author = "Zhang, Zhe and
Singh, Munindar",
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/D18-1378",
doi = "10.18653/v1/D18-1378",
pages = "3412--3422",
abstract = "We propose Limbic, an unsupervised probabilistic model that addresses the problem of discovering aspects and sentiments and associating them with authors of opinionated texts. Limbic combines three ideas, incorporating authors, discourse relations, and word embeddings. For discourse relations, Limbic adopts a generative process regularized by a Markov Random Field. To promote words with high semantic similarity into the same topic, Limbic captures semantic regularities from word embeddings via a generalized P{\'o}lya Urn process. We demonstrate that Limbic (1) discovers aspects associated with sentiments with high lexical diversity; (2) outperforms state-of-the-art models by a substantial margin in topic cohesion and sentiment classification.",
}
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%0 Conference Proceedings
%T Limbic: Author-Based Sentiment Aspect Modeling Regularized with Word Embeddings and Discourse Relations
%A Zhang, Zhe
%A Singh, Munindar
%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 zhang-singh-2018-limbic
%X We propose Limbic, an unsupervised probabilistic model that addresses the problem of discovering aspects and sentiments and associating them with authors of opinionated texts. Limbic combines three ideas, incorporating authors, discourse relations, and word embeddings. For discourse relations, Limbic adopts a generative process regularized by a Markov Random Field. To promote words with high semantic similarity into the same topic, Limbic captures semantic regularities from word embeddings via a generalized Pólya Urn process. We demonstrate that Limbic (1) discovers aspects associated with sentiments with high lexical diversity; (2) outperforms state-of-the-art models by a substantial margin in topic cohesion and sentiment classification.
%R 10.18653/v1/D18-1378
%U https://aclanthology.org/D18-1378
%U https://doi.org/10.18653/v1/D18-1378
%P 3412-3422
Markdown (Informal)
[Limbic: Author-Based Sentiment Aspect Modeling Regularized with Word Embeddings and Discourse Relations](https://aclanthology.org/D18-1378) (Zhang & Singh, EMNLP 2018)
ACL