Topic Modeling Using Distributed Word Embeddings

2016-03-15 · Ramandeep S Randhawa, Parag Jain, Gagan Madan

We propose a new algorithm for topic modeling, Vec2Topic, that identifies the main topics in a corpus using semantic information captured via high-dimensional distributed word embeddings. Our technique is unsupervised and generates a list of topics ranked with respect to importance. We find that it works better than existing topic modeling techniques such as Latent Dirichlet Allocation for identifying key topics in user-generated content, such as emails, chats, etc., where topics are diffused across the corpus. We also find that Vec2Topic works equally well for non-user generated content, such as papers, reports, etc., and for small corpora such as a single-document.

📄 PDF Abstract BibTeX arXiv:1603.04747

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Tasks

Word Embeddings

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Distributed word embeddings have yielded state-of-the-art performance in many NLP tasks, mainly due to their success in capturing useful semantic information. These representations assign only a single vector to each wor…

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Gaussian LDA integrates topic modeling with word embeddings by replacing discrete topic distribution over word types with multivariate Gaussian distribution on the embedding space. This can take semantic information of w…

Topic Models Word Embeddings

Multi-view and Multi-source Transfers in Neural Topic Modeling with Pretrained Topic and Word Embeddings

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