# ANTM: An Aligned Neural Topic Model for Exploring Evolving Topics

arXiv: https://arxiv.org/abs/2302.01501 · HTML: https://mlsanity.com/paper/2302.01501/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 61 references that are in the map.
- First posted on arXiv 3 Feb 2023.

## Builds on (most cited first, 17 of 17)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [BERTopic: Neural topic modeling with a class-based TF-IDF procedure](https://mlsanity.com/paper/2203.05794/) (2022)
- [data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language](https://mlsanity.com/paper/2202.03555/) (2022)
- [Topic Modeling in Embedding Spaces](https://mlsanity.com/paper/1907.04907/) (2019)
- [Top2Vec: Distributed Representations of Topics](https://mlsanity.com/paper/2008.09470/) (2020)
- [On the Use of ArXiv as a Dataset](https://mlsanity.com/paper/1905.00075/) (2019)
- [The Dynamic Embedded Topic Model](https://mlsanity.com/paper/1907.05545/) (2019)
- [Topic Modeling with Contextualized Word Representation Clusters](https://mlsanity.com/paper/2010.12626/) (2020)
- [NYTWIT: A Dataset of Novel Words in the New York Times](https://mlsanity.com/paper/2003.03444/) (2020)
- [Context Reinforced Neural Topic Modeling over Short Texts](https://mlsanity.com/paper/2008.04545/) (2020)
- [Scalable Generalized Dynamic Topic Models](https://mlsanity.com/paper/1803.07868/) (2018)
- [Scaling up Dynamic Topic Models](https://mlsanity.com/paper/1602.06049/) (2016)
- [Exploring the Political Agenda of the European Parliament Using a Dynamic Topic Modeling Approach](https://mlsanity.com/paper/1607.03055/) (2016)
- [Dynamic topic modeling of the COVID-19 Twitter narrative among U.S. governors and cabinet executives](https://mlsanity.com/paper/2004.11692/) (2020)
- [Manifold-aligned Neighbor Embedding](https://mlsanity.com/paper/2205.11257/) (2022)

## Built on by (most cited first, 6 of 6)

- [Contextualized Topic Coherence Metrics](https://mlsanity.com/paper/2305.14587/) (2023)
- [A Survey on Neural Topic Models: Methods, Applications, and Challenges](https://mlsanity.com/paper/2401.15351/) (2024)
- [Modeling Dynamic Topics in Chain-Free Fashion by Evolution-Tracking Contrastive Learning and Unassociated Word Exclusion](https://mlsanity.com/paper/2405.17957/) (2024)
- [Tethering Broken Themes: Aligning Neural Topic Models with Labels and Authors](https://mlsanity.com/paper/2410.18140/) (2024)
- [ATEM: A Topic Evolution Model for the Detection of Emerging Topics in Scientific Archives](https://mlsanity.com/paper/2306.02221/) (2023)
- [Continual Neural Topic Model](https://mlsanity.com/paper/2508.15612/) (2025)

## Abstract (arXiv)

This paper presents an algorithmic family of dynamic topic models called Aligned Neural Topic Models (ANTM), which combine novel data mining algorithms to provide a modular framework for discovering evolving topics. ANTM maintains the temporal continuity of evolving topics by extracting time-aware features from documents using advanced pre-trained Large Language Models (LLMs) and employing an overlapping sliding window algorithm for sequential document clustering. This overlapping sliding window algorithm identifies a different number of topics within each time frame and aligns semantically similar document clusters across time periods. This process captures emerging and fading trends across different periods and allows for a more interpretable representation of evolving topics. Experiments on four distinct datasets show that ANTM outperforms probabilistic dynamic topic models in terms of topic coherence and diversity metrics. Moreover, it improves the scalability and flexibility of dynamic topic models by being accessible and adaptable to different types of algorithms. Additionally, a Python package is developed for researchers and scientists who wish to study the trends and evolving patterns of topics in large-scale textual data.