Deep Temporal-Recurrent-Replicated-Softmax for Topical Trends over Time
Pankaj Gupta, Subburam Rajaram, Hinrich Schütze, Bernt Andrassy
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.- Anthology ID:
- N18-1098
- Volume:
- Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
- Month:
- June
- Year:
- 2018
- Address:
- New Orleans, Louisiana
- Editors:
- Marilyn Walker, Heng Ji, Amanda Stent
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1079–1089
- Language:
- URL:
- https://aclanthology.org/N18-1098
- DOI:
- 10.18653/v1/N18-1098
- Cite (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.
- Cite (Informal):
- Deep Temporal-Recurrent-Replicated-Softmax for Topical Trends over Time (Gupta et al., NAACL 2018)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/N18-1098.pdf