@inproceedings{gupta-etal-2018-deep-temporal,
title = "Deep Temporal-Recurrent-Replicated-Softmax for Topical Trends over Time",
author = {Gupta, Pankaj and
Rajaram, Subburam and
Sch{\"u}tze, Hinrich and
Andrassy, Bernt},
editor = "Walker, Marilyn and
Ji, Heng and
Stent, Amanda",
booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N18-1098",
doi = "10.18653/v1/N18-1098",
pages = "1079--1089",
abstract = "Dynamic topic modeling facilitates the identification of topical trends over time in temporal collections of unstructured documents. We introduce a novel unsupervised neural dynamic topic model named as Recurrent Neural Network-Replicated Softmax Model (RNNRSM), where the discovered topics at each time influence the topic discovery in the subsequent time steps. We account for the temporal ordering of documents by explicitly modeling a joint distribution of latent topical dependencies over time, using distributional estimators with temporal recurrent connections. Applying RNN-RSM to 19 years of articles on NLP research, we demonstrate that compared to state-of-the art topic models, RNNRSM shows better generalization, topic interpretation, evolution and trends. We also introduce a metric (named as SPAN) to quantify the capability of dynamic topic model to capture word evolution in topics over time.",
}
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<abstract>Dynamic topic modeling facilitates the identification of topical trends over time in temporal collections of unstructured documents. We introduce a novel unsupervised neural dynamic topic model named as Recurrent Neural Network-Replicated Softmax Model (RNNRSM), where the discovered topics at each time influence the topic discovery in the subsequent time steps. We account for the temporal ordering of documents by explicitly modeling a joint distribution of latent topical dependencies over time, using distributional estimators with temporal recurrent connections. Applying RNN-RSM to 19 years of articles on NLP research, we demonstrate that compared to state-of-the art topic models, RNNRSM shows better generalization, topic interpretation, evolution and trends. We also introduce a metric (named as SPAN) to quantify the capability of dynamic topic model to capture word evolution in topics over time.</abstract>
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%0 Conference Proceedings
%T Deep Temporal-Recurrent-Replicated-Softmax for Topical Trends over Time
%A Gupta, Pankaj
%A Rajaram, Subburam
%A Schütze, Hinrich
%A Andrassy, Bernt
%Y Walker, Marilyn
%Y Ji, Heng
%Y Stent, Amanda
%S Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F gupta-etal-2018-deep-temporal
%X Dynamic topic modeling facilitates the identification of topical trends over time in temporal collections of unstructured documents. We introduce a novel unsupervised neural dynamic topic model named as Recurrent Neural Network-Replicated Softmax Model (RNNRSM), where the discovered topics at each time influence the topic discovery in the subsequent time steps. We account for the temporal ordering of documents by explicitly modeling a joint distribution of latent topical dependencies over time, using distributional estimators with temporal recurrent connections. Applying RNN-RSM to 19 years of articles on NLP research, we demonstrate that compared to state-of-the art topic models, RNNRSM shows better generalization, topic interpretation, evolution and trends. We also introduce a metric (named as SPAN) to quantify the capability of dynamic topic model to capture word evolution in topics over time.
%R 10.18653/v1/N18-1098
%U https://aclanthology.org/N18-1098
%U https://doi.org/10.18653/v1/N18-1098
%P 1079-1089
Markdown (Informal)
[Deep Temporal-Recurrent-Replicated-Softmax for Topical Trends over Time](https://aclanthology.org/N18-1098) (Gupta et al., NAACL 2018)
ACL
- Pankaj Gupta, Subburam Rajaram, Hinrich Schütze, and Bernt Andrassy. 2018. Deep Temporal-Recurrent-Replicated-Softmax for Topical Trends over Time. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 1079–1089, New Orleans, Louisiana. Association for Computational Linguistics.