Topic2Vec: Learning Distributed Representations of Topics
This work addresses the need for better topic representations in NLP and ML, but it appears incremental as it extends embedding methods to topics.
The authors tackled the problem of representing topics in a semantic vector space as an alternative to probability distributions from LDA, and their Topic2Vec approach achieved interesting and meaningful results.
Latent Dirichlet Allocation (LDA) mining thematic structure of documents plays an important role in nature language processing and machine learning areas. However, the probability distribution from LDA only describes the statistical relationship of occurrences in the corpus and usually in practice, probability is not the best choice for feature representations. Recently, embedding methods have been proposed to represent words and documents by learning essential concepts and representations, such as Word2Vec and Doc2Vec. The embedded representations have shown more effectiveness than LDA-style representations in many tasks. In this paper, we propose the Topic2Vec approach which can learn topic representations in the same semantic vector space with words, as an alternative to probability. The experimental results show that Topic2Vec achieves interesting and meaningful results.