Topic modeling for scientific articles
- License: BSD license
- Documentation: https://berteley.readthedocs.io.
pip install berteley
# code snippet showing very basic usageTopic Modeling Colab Notebook: TBD
- A text pre-processing suite tailored for scientific articles. Including a curated stopword list.
- 3 readily available language models that are trained on scientific articles.
- Real time metric calculation for topic model comparison.
BERTeley Copyright (c) 2023, The Regents of the University of California, through Lawrence Berkeley National Laboratory (subject to receipt of any required approvals from the U.S. Dept. of Energy) and University of California, Berkeley. All rights reserved.
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Please reference this work:
@article{Berteley,
title = {Benchmarking topic models on scientific articles using BERTeley},
journal = {Natural Language Processing Journal},
volume = {6},
pages = {100044},
year = {2024},
issn = {2949-7191},
doi = {https://doi.org/10.1016/j.nlp.2023.100044},
url = {https://www.sciencedirect.com/science/article/pii/S2949719123000419},
author = {Eric Chagnon and Ronald Pandolfi and Jeffrey Donatelli and Daniela Ushizima},
keywords = {NLP, Topic modeling, Scientific articles, Transformers},
abstract = {The introduction of BERTopic marked a crucial advancement in topic modeling and presented a topic model that outperformed both traditional and modern topic models in terms of topic modeling metrics on a variety of corpora. However, unique issues arise when topic modeling is performed on scientific articles. This paper introduces BERTeley, an innovative tool built upon BERTopic, designed to alleviate these shortcomings and improve the usability of BERTopic when conducting topic modeling on a corpus consisting of scientific articles. This is accomplished through BERTeley’s three main features: scientific article preprocessing, topic modeling using pre-trained scientific language models, and topic model metric calculation. Furthermore, an experiment was conducted comparing topic models using four different language models in three corpora consisting of scientific articles.}
}
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