A python package to run contextualized topic modeling. CTMs combine contextualized embeddings (e.g., BERT) with topic models to get coherent topics. Published at EACL and ACL 2021 (Bianchi et al.).

OCTIS: Comparing Topic Models is Simple! A python package to optimize and evaluate topic models (accepted at EACL2021 demo track)

A Topic Modeling System Toolkit

Papers of Neural Topic Models (NTMs)

Code for Short Text Topic Modeling with Topic Distribution Quantization and Negative Sampling Decoder (EMNLP2020).

Code for Effective Neural Topic Modeling with Embedding Clustering Regularization (ICML2023)

A Fast, Adaptive, Stable, and Transferable Topic Model

Aligned Neural Topic Model (ANTM) for Exploring Evolving Topics: a dynamic neural topic model that uses document embeddings (data2vec) to compute clusters of semantically similar documents at different periods, and aligns document clusters to represent topic evolution.

A versatile Python package engineered for seamless topic modeling, topic evaluation, and topic visualization. Ideal for text analysis, natural language processing (NLP), and research in the social sciences, STREAM simplifies the extraction, interpretation, and visualization of topics from large, complex datasets.

neural_topic_models

Code for On the Affinity, Rationality, and Diversity of Hierarchical Topic Modeling (AAAI 2024)

"Modeling electronic health record data using an end-to-end knowledge-graph-informed topic model" paper on Sci Rep (2022)

This repository is associated with the paper "Do Neural Topic Models Really Need Dropout? Analysis of the Effect of Dropout in Topic Modeling", accepted at EACL 2023.