# Improving Topic Models with Latent Feature Word Representations

arXiv: https://arxiv.org/abs/1810.06306 · HTML: https://mlsanity.com/paper/1810.06306/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Oct 2018.

## Built on by (most cited first, 28 of 28)

- [BERTopic: Neural topic modeling with a class-based TF-IDF procedure](https://mlsanity.com/paper/2203.05794/) (2022)
- [Is Automated Topic Model Evaluation Broken?: The Incoherence of Coherence](https://mlsanity.com/paper/2107.02173/) (2021)
- [Topic Modelling Meets Deep Neural Networks: A Survey](https://mlsanity.com/paper/2103.00498/) (2021)
- [Representing Mixtures of Word Embeddings with Mixtures of Topic Embeddings](https://mlsanity.com/paper/2203.01570/) (2022)
- [Topic Discovery via Latent Space Clustering of Pretrained Language Model Representations](https://mlsanity.com/paper/2202.04582/) (2022)
- [vONTSS: vMF based semi-supervised neural topic modeling with optimal transport](https://mlsanity.com/paper/2307.01226/) (2023)
- [DeTiME: Diffusion-Enhanced Topic Modeling using Encoder-decoder based LLM](https://mlsanity.com/paper/2310.15296/) (2023)
- [HyperMiner: Topic Taxonomy Mining with Hyperbolic Embedding](https://mlsanity.com/paper/2210.10625/) (2022)
- [Effective Seed-Guided Topic Discovery by Integrating Multiple Types of Contexts](https://mlsanity.com/paper/2212.06002/) (2022)
- [Seed-Guided Topic Discovery with Out-of-Vocabulary Seeds](https://mlsanity.com/paper/2205.01845/) (2022)
- [CWTM: Leveraging Contextualized Word Embeddings from BERT for Neural Topic Modeling](https://mlsanity.com/paper/2305.09329/) (2023)
- [Multi-source Neural Topic Modeling in Multi-view Embedding Spaces](https://mlsanity.com/paper/2104.08551/) (2021)
- [DGNet: Distribution Guided Efficient Learning for Oil Spill Image Segmentation](https://mlsanity.com/paper/2301.01202/) (2022)
- [Short Text Clustering with Transformers](https://mlsanity.com/paper/2102.00541/) (2021)
- [Evaluation of Thematic Coherence in Microblogs](https://mlsanity.com/paper/2106.15971/) (2021)
- [Neural-Bayesian Program Learning for Few-shot Dialogue Intent Parsing](https://mlsanity.com/paper/2410.06190/) (2024)
- [Toward Purpose-oriented Topic Model Evaluation enabled by Large Language Models](https://mlsanity.com/paper/2509.07142/) (2025)
- [Enhance Topics Analysis based on Keywords Properties](https://mlsanity.com/paper/2203.04786/) (2022)
- [Revealing Patient-Reported Experiences in Healthcare from Social Media using the DAPMAV Framework](https://mlsanity.com/paper/2210.04232/) (2022)
- [Chronic pain patient narratives allow for the estimation of current pain intensity](https://mlsanity.com/paper/2210.17473/) (2022)
- [Efficient and Flexible Topic Modeling using Pretrained Embeddings and Bag of Sentences](https://mlsanity.com/paper/2302.03106/) (2023)
- [Towards Generalising Neural Topical Representations](https://mlsanity.com/paper/2307.12564/) (2023)
- [Chronic Pain and Language: A Topic Modelling Approach to Personal Pain Descriptions](https://mlsanity.com/paper/2109.00402/) (2021)
- [Bilingual Topic Models for Comparable Corpora](https://mlsanity.com/paper/2111.15278/) (2021)
- [How People Respond to the COVID-19 Pandemic on Twitter: A Comparative Analysis of Emotional Expressions from US and India](https://mlsanity.com/paper/2303.10560/) (2023)
- [A Novel Method for News Article Event-Based Embedding](https://mlsanity.com/paper/2405.13071/) (2024)
- [Fine-Tuning Topics through Weighting Aspect Keywords](https://mlsanity.com/paper/2502.08496/) (2025)
- [Interpretable Topic Extraction and Word Embedding Learning using row-stochastic DEDICOM](https://mlsanity.com/paper/2507.16695/) (2025)

## Abstract (arXiv)

Probabilistic topic models are widely used to discover latent topics in document collections, while latent feature vector representations of words have been used to obtain high performance in many NLP tasks. In this paper, we extend two different Dirichlet multinomial topic models by incorporating latent feature vector representations of words trained on very large corpora to improve the word-topic mapping learnt on a smaller corpus. Experimental results show that by using information from the external corpora, our new models produce significant improvements on topic coherence, document clustering and document classification tasks, especially on datasets with few or short documents.