# Keyword Assisted Embedded Topic Model

arXiv: https://arxiv.org/abs/2112.03101 · HTML: https://mlsanity.com/paper/2112.03101/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 21 references that are in the map.
- First posted on arXiv 22 Nov 2021.

## Builds on (most cited first, 9 of 9)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Topic Modeling in Embedding Spaces](https://mlsanity.com/paper/1907.04907/) (2019)
- [Autoencoding Variational Inference For Topic Models](https://mlsanity.com/paper/1703.01488/) (2017)
- [Summarizing Opinions: Aspect Extraction Meets Sentiment Prediction and They Are Both Weakly Supervised](https://mlsanity.com/paper/1808.08858/) (2018)
- [Discriminative Topic Mining via Category-Name Guided Text Embedding](https://mlsanity.com/paper/1908.07162/) (2019)
- [Keyword Assisted Topic Models](https://mlsanity.com/paper/2004.05964/) (2020)
- [Weakly-Supervised Aspect-Based Sentiment Analysis via Joint Aspect-Sentiment Topic Embedding](https://mlsanity.com/paper/2010.06705/) (2020)
- [Towards Autoencoding Variational Inference for Aspect-based Opinion Summary](https://mlsanity.com/paper/1902.02507/) (2019)

## Built on by (most cited first, 9 of 9)

- [vONTSS: vMF based semi-supervised neural topic modeling with optimal transport](https://mlsanity.com/paper/2307.01226/) (2023)
- [Effective Seed-Guided Topic Discovery by Integrating Multiple Types of Contexts](https://mlsanity.com/paper/2212.06002/) (2022)
- [A Survey on Retrieval And Structuring Augmented Generation with Large Language Models](https://mlsanity.com/paper/2509.10697/) (2025)
- [Coordinated Topic Modeling](https://mlsanity.com/paper/2210.08559/) (2022)
- [S2vNTM: Semi-supervised vMF Neural Topic Modeling](https://mlsanity.com/paper/2307.04804/) (2023)
- [TopicAdapt- An Inter-Corpora Topics Adaptation Approach](https://mlsanity.com/paper/2310.04978/) (2023)
- [Interactive Topic Models with Optimal Transport](https://mlsanity.com/paper/2406.19928/) (2024)
- [Enhancing Topic Interpretability for Neural Topic Modeling through Topic-wise Contrastive Learning](https://mlsanity.com/paper/2412.17338/) (2024)
- [Seeded Poisson Factorization: leveraging domain knowledge to fit topic models](https://mlsanity.com/paper/2503.02741/) (2025)

## Abstract (arXiv)

By illuminating latent structures in a corpus of text, topic models are an essential tool for categorizing, summarizing, and exploring large collections of documents. Probabilistic topic models, such as latent Dirichlet allocation (LDA), describe how words in documents are generated via a set of latent distributions called topics. Recently, the Embedded Topic Model (ETM) has extended LDA to utilize the semantic information in word embeddings to derive semantically richer topics. As LDA and its extensions are unsupervised models, they aren't defined to make efficient use of a user's prior knowledge of the domain. To this end, we propose the Keyword Assisted Embedded Topic Model (KeyETM), which equips ETM with the ability to incorporate user knowledge in the form of informative topic-level priors over the vocabulary. Using both quantitative metrics and human responses on a topic intrusion task, we demonstrate that KeyETM produces better topics than other guided, generative models in the literature.