# A Framework for Neural Topic Modeling of Text Corpora

arXiv: https://arxiv.org/abs/2108.08946 · HTML: https://mlsanity.com/paper/2108.08946/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 35 references that are in the map.
- First posted on arXiv 19 Aug 2021.

## Builds on (most cited first, 6 of 6)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation](https://mlsanity.com/paper/2004.09813/) (2020)
- [Latent Dirichlet Allocation (LDA) and Topic modeling: models, applications, a survey](https://mlsanity.com/paper/1711.04305/) (2017)
- [Unveiling the Political Agenda of the European Parliament Plenary: A Topical Analysis](https://mlsanity.com/paper/1505.07302/) (2015)

## Abstract (arXiv)

Topic Modeling refers to the problem of discovering the main topics that have occurred in corpora of textual data, with solutions finding crucial applications in numerous fields. In this work, inspired by the recent advancements in the Natural Language Processing domain, we introduce FAME, an open-source framework enabling an efficient mechanism of extracting and incorporating textual features and utilizing them in discovering topics and clustering text documents that are semantically similar in a corpus. These features range from traditional approaches (e.g., frequency-based) to the most recent auto-encoding embeddings from transformer-based language models such as BERT model family. To demonstrate the effectiveness of this library, we conducted experiments on the well-known News-Group dataset. The library is available online.