@inproceedings{srivatsan-etal-2018-modeling,
title = "Modeling Online Discourse with Coupled Distributed Topics",
author = "Srivatsan, Nikita and
Wojtowicz, Zachary and
Berg-Kirkpatrick, Taylor",
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/D18-1496",
doi = "10.18653/v1/D18-1496",
pages = "4673--4682",
abstract = "In this paper, we propose a deep, globally normalized topic model that incorporates structural relationships connecting documents in socially generated corpora, such as online forums. Our model (1) captures discursive interactions along observed reply links in addition to traditional topic information, and (2) incorporates latent distributed representations arranged in a deep architecture, which enables a GPU-based mean-field inference procedure that scales efficiently to large data. We apply our model to a new social media dataset consisting of 13M comments mined from the popular internet forum Reddit, a domain that poses significant challenges to models that do not account for relationships connecting user comments. We evaluate against existing methods across multiple metrics including perplexity and metadata prediction, and qualitatively analyze the learned interaction patterns.",
}
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<abstract>In this paper, we propose a deep, globally normalized topic model that incorporates structural relationships connecting documents in socially generated corpora, such as online forums. Our model (1) captures discursive interactions along observed reply links in addition to traditional topic information, and (2) incorporates latent distributed representations arranged in a deep architecture, which enables a GPU-based mean-field inference procedure that scales efficiently to large data. We apply our model to a new social media dataset consisting of 13M comments mined from the popular internet forum Reddit, a domain that poses significant challenges to models that do not account for relationships connecting user comments. We evaluate against existing methods across multiple metrics including perplexity and metadata prediction, and qualitatively analyze the learned interaction patterns.</abstract>
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%0 Conference Proceedings
%T Modeling Online Discourse with Coupled Distributed Topics
%A Srivatsan, Nikita
%A Wojtowicz, Zachary
%A Berg-Kirkpatrick, Taylor
%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 srivatsan-etal-2018-modeling
%X In this paper, we propose a deep, globally normalized topic model that incorporates structural relationships connecting documents in socially generated corpora, such as online forums. Our model (1) captures discursive interactions along observed reply links in addition to traditional topic information, and (2) incorporates latent distributed representations arranged in a deep architecture, which enables a GPU-based mean-field inference procedure that scales efficiently to large data. We apply our model to a new social media dataset consisting of 13M comments mined from the popular internet forum Reddit, a domain that poses significant challenges to models that do not account for relationships connecting user comments. We evaluate against existing methods across multiple metrics including perplexity and metadata prediction, and qualitatively analyze the learned interaction patterns.
%R 10.18653/v1/D18-1496
%U https://aclanthology.org/D18-1496
%U https://doi.org/10.18653/v1/D18-1496
%P 4673-4682
Markdown (Informal)
[Modeling Online Discourse with Coupled Distributed Topics](https://aclanthology.org/D18-1496) (Srivatsan et al., EMNLP 2018)
ACL
- Nikita Srivatsan, Zachary Wojtowicz, and Taylor Berg-Kirkpatrick. 2018. Modeling Online Discourse with Coupled Distributed Topics. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 4673–4682, Brussels, Belgium. Association for Computational Linguistics.