Combing LDA and Word Embeddings for topic modeling – Towards Data Science


Latent Dirichlet Allocation (LDA) is a classical way to do a topic modelling. Topic modeling is a unsupervised learning and the goal is group different document to same "topic". Typical example is clustering a news to corresponding category including "Finance", "Travel", "Sport" etc. Before word embeddings we may use Bag-of-Words in most of the time. However, the world changed after Mikolov et al. introduce word2vec (one of the example of Word Embeddings) in 2013.

Duplicate Docs Excel Report

None found

Similar Docs  Excel Report  more

None found