In BERTopic, you have several options to nudge the creation of topics toward certain pre-specified topics. Here, we will be looking at semi-supervised topic modeling with BERTopic.
Here, we will be looking at semi-supervised topic modeling with BERTopic. Semi-supervised modeling allows us to steer the dimensionality reduction of the embeddings into a space that closely follows any labels you might already have.
Such details provide a deeper understanding and appreciation for Semi-Supervised Topic Modeling.
In this paper, we propose Semi-Supervised vMF Neural Topic Modeling (S2vNTM) to overcome these difficulties. S2vNTM takes a few seed keywords as input for topics. S2vNTM leverages the pattern of keywords to identify potential topics, as well as optimize the quality of topics' keywords sets. In this paper, we have focused our algorithm design and experiments on the semi-supervised training of topic models. But by design, our prediction-constrained framework is directly applicable to an enormous range of latent variable models. This work presents a semi-supervised neural topic modeling method, vONTSS, which uses von Mises-Fisher (vMF) based variational autoencoders and optimal transport. In this paper, we propose a novel methodology for topic detection designed to improve semi-supervised clustering of users' reviews with an application for tourism review data. In this post, Ill show how to overcome some of these challenges with whats known as a semi-supervised approach.
Furthermore, visual representations like the one above help us fully grasp the concept of Semi-Supervised Topic Modeling.
In this paper, we have focused our algorithm design and experiments on the semi-supervised training of topic models. But by design, our prediction-constrained framework is directly applicable to an enormous range of latent variable models.
Such details provide a deeper understanding and appreciation for Semi-Supervised Topic Modeling.
In this paper, we propose a novel methodology for topic detection designed to improve semi-supervised clustering of users' reviews with an application for tourism review data.
