# Topic2Vec: Learning Distributed Representations of Topics

arXiv: https://arxiv.org/abs/1506.08422 · HTML: https://mlsanity.com/paper/1506.08422/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jun 2015.

## Built on by (most cited first, 6 of 6)

- [Repo2Vec: A Comprehensive Embedding Approach for Determining Repository Similarity](https://mlsanity.com/paper/2107.05112/) (2021)
- [Word embeddings for topic modeling: an application to the estimation of the economic policy uncertainty index](https://mlsanity.com/paper/2111.00057/) (2021)
- [Topic Modeling with Fine-tuning LLMs and Bag of Sentences](https://mlsanity.com/paper/2408.03099/) (2024)
- [Network representation learning systematic review: ancestors and current development state](https://mlsanity.com/paper/2109.07583/) (2021)
- [Efficient and Flexible Topic Modeling using Pretrained Embeddings and Bag of Sentences](https://mlsanity.com/paper/2302.03106/) (2023)
- [Unveiling Novelty Evolution in the field of Library and Information Science in China](https://mlsanity.com/paper/2606.29872/) (2026)

## Abstract (arXiv)

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.