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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

Encoder-Decoder model with local and pairwise loss along with shared encoder and discriminator network (EDLPS)

Understanding EDLPS: A Novel Method for Obtaining Semantic Sentence Embeddings If you're interested in natural language processing, you've probably heard of word embeddings. Word embeddings are a way to represent words as numerical vectors, which can then be used as inputs to machine learning models. These embeddings have been found to be incredibly useful, and there are many different methods for obtaining them. However, obtaining sentence-level embeddings is still a relatively new area of res

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