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BERTopic: Neural topic modeling with a class-based TF-IDF procedure. (arXiv:2203.05794v1 [cs.CL])
March 14, 2022, 1:11 a.m. | Maarten Grootendorst
cs.CL updates on arXiv.org arxiv.org
Topic models can be useful tools to discover latent topics in collections of
documents. Recent studies have shown the feasibility of approach topic modeling
as a clustering task. We present BERTopic, a topic model that extends this
process by extracting coherent topic representation through the development of
a class-based variation of TF-IDF. More specifically, BERTopic generates
document embedding with pre-trained transformer-based language models, clusters
these embeddings, and finally, generates topic representations with the
class-based TF-IDF procedure. BERTopic generates coherent topics …
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