---
title: "Who cites Improving Topic Models with — 36 citing papers"
description: "36 papers of the Elamur Research library cite «Improving Topic Models with Latent Feature Word Representations», found in their…"
canonical: "https://research.elamur.ai/free/en/papers/nguyen-2015-improving-topic-models-latent-feature/cited-by/"
---
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[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Improving Topic Models with Latent Feature Word…](https://md.research.elamur.ai/free/en/papers/nguyen-2015-improving-topic-models-latent-feature/) / cited by

# Who cites «Improving Topic Models with Latent Feature Word Representations»

36 papers of our library cite it (of the 149,439 whose bibliographies we read) · 2 with a page here, the most cited first · [← back to «Improving Topic Models with Latent Feature Word Representations»](https://md.research.elamur.ai/free/en/papers/nguyen-2015-improving-topic-models-latent-feature/)

1. [**BERTopic: Neural topic modeling with a class-based TF-IDF procedure arXiv:2203.05794**Maarten Grootendorst*2022 · 1,737 citations*](https://md.research.elamur.ai/free/en/papers/grootendorst-2022-bertopic-neural-topic-modeling-class/)
2. **[Pre-training is a Hot Topic: Contextualized Document Embeddings Improve Topic Coherence](https://md.research.elamur.ai/free/en/papers/bianchi-2021-pre-training-is-hot-topic/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Federico Bianchi, Silvia Terragni, Dirk Hovy*2021 · 366 citations*

34 more papers of our library cite it; they have no page here yet.

- [← back to](https://md.research.elamur.ai/free/en/papers/nguyen-2015-improving-topic-models-latent-feature/) — Improving Topic Models with Latent Feature Word Representations