Topic2Vec: Learning Distributed Representations of Topics

TL;DR

This paper introduces Topic2Vec, a novel embedding method that learns topic representations in the same semantic space as words, offering a more effective alternative to traditional LDA-based features for text analysis.

Contribution

The paper proposes Topic2Vec, a new approach to learn topic embeddings in the same space as words, improving upon LDA for capturing semantic relationships.

Findings

Topic2Vec produces meaningful topic representations.

It outperforms LDA in capturing semantic relationships.

Experimental results validate the effectiveness of Topic2Vec.

Abstract

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.