# Variational Deep Semantic Hashing for Text Documents

arXiv: https://arxiv.org/abs/1708.03436 · HTML: https://mlsanity.com/paper/1708.03436/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Aug 2017.

## Built on by (most cited first, 12 of 12)

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- [Integrating Semantics and Neighborhood Information with Graph-Driven Generative Models for Document Retrieval](https://mlsanity.com/paper/2105.13066/) (2021)
- [Representation Learning for Efficient and Effective Similarity Search and Recommendation](https://mlsanity.com/paper/2109.01815/) (2021)
- [Semantic Watermarking for Malicious Image Manipulation Detection](https://mlsanity.com/paper/2609.39623/) (2026)

## Abstract (arXiv)

As the amount of textual data has been rapidly increasing over the past decade, efficient similarity search methods have become a crucial component of large-scale information retrieval systems. A popular strategy is to represent original data samples by compact binary codes through hashing. A spectrum of machine learning methods have been utilized, but they often lack expressiveness and flexibility in modeling to learn effective representations. The recent advances of deep learning in a wide range of applications has demonstrated its capability to learn robust and powerful feature representations for complex data. Especially, deep generative models naturally combine the expressiveness of probabilistic generative models with the high capacity of deep neural networks, which is very suitable for text modeling. However, little work has leveraged the recent progress in deep learning for text hashing. In this paper, we propose a series of novel deep document generative models for text hashing. The first proposed model is unsupervised while the second one is supervised by utilizing document labels/tags for hashing. The third model further considers document-specific factors that affect the generation of words. The probabilistic generative formulation of the proposed models provides a principled framework for model extension, uncertainty estimation, simulation, and interpretability. Based on variational inference and reparameterization, the proposed models can be interpreted as encoder-decoder deep neural networks and thus they are capable of learning complex nonlinear distributed representations of the original documents. We conduct a comprehensive set of experiments on four public testbeds. The experimental results have demonstrated the effectiveness of the proposed supervised learning models for text hashing.