# Dynamic and Static Topic Model for Analyzing Time-Series Document Collections

arXiv: https://arxiv.org/abs/1805.02203 · HTML: https://mlsanity.com/paper/1805.02203/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 May 2018.

## Built on by (most cited first, 4 of 4)

- [Modeling Dynamic Topics in Chain-Free Fashion by Evolution-Tracking Contrastive Learning and Unassociated Word Exclusion](https://mlsanity.com/paper/2405.17957/) (2024)
- [The future is different: Large pre-trained language models fail in prediction tasks](https://mlsanity.com/paper/2211.00384/) (2022)
- [Neural Dynamic Focused Topic Model](https://mlsanity.com/paper/2301.10988/) (2023)
- [Embedded Topic Models Enhanced by Wikification](https://mlsanity.com/paper/2410.02441/) (2024)

## Abstract (arXiv)

For extracting meaningful topics from texts, their structures should be considered properly. In this paper, we aim to analyze structured time-series documents such as a collection of news articles and a series of scientific papers, wherein topics evolve along time depending on multiple topics in the past and are also related to each other at each time. To this end, we propose a dynamic and static topic model, which simultaneously considers the dynamic structures of the temporal topic evolution and the static structures of the topic hierarchy at each time. We show the results of experiments on collections of scientific papers, in which the proposed method outperformed conventional models. Moreover, we show an example of extracted topic structures, which we found helpful for analyzing research activities.