topic-embeddings

Contextualized Topic Models

Understanding Contextualized Topic Models In recent years, advancements in machine learning and natural language processing have led to the development of a new approach to analyzing text called Contextualized Topic Models. This approach utilizes neural networks to identify patterns and themes within text based on the context in which the words are used. How Contextualized Topic Models Work The approach used by Contextualized Topic Models is based on a Neural-ProdLDA variational autoencoding

lda2vec

What is lda2vec? lda2vec is a machine learning algorithm that creates word vectors while also taking into account the topic of the document that the word is from. It combines two popular algorithms: word2vec and Latent Dirichlet Allocation (LDA). Word2vec is an algorithm used for language modeling, which tries to predict the probability of a word being used in context. It creates a set of word vectors that are representations of words in a high-dimensional space. This means that words similar

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