Variational Deep Semantic Hashing for Text Documents

By Suthee Chaidaroon, Yi Fang. 2017

79 citations recorded by the source corpus.

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.

Subfields: Computer Vision and Pattern Recognition, Natural Language Processing

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Citations in the corpus

A Retrieve-and-Edit Framework for Predicting Structured Outputs (2018, background): “Semantic hashing by autoencoders [16] is a related idea where an autoencoder’s latent representation is used to construct a hash function to identify similar images or texts [29, 6].”
NASH: Toward End-to-End Neural Architecture for Generative Semantic Hashing (2018, methodology): “, 2015); (ii) continuous text representations are first inferred, which are binarized as a second (separate) step during testing (Wang et al., 2013; Chaidaroon and Fang, 2017).”
An Introduction to Neural Information Retrieval (2018, unknown): “Variational autoencoders have also been explored [Chaidaroon and Fang, 2017] on similar lines.”
EXS: Explainable Search Using Local Model Agnostic Interpretability (2018, background): “alnetworkshavebeenwidelysuccessfulinanarrayoflearning tasksincludingvision,speechandNLP.Recently,theIRcommunity has also seen successful application of deep learning models in document representation [3, 9], query modeling [4, 19, 20] and design of ranking models [6, 10, 11, 16]. It is safe to assum”
All the News That Fits in Bits: Learned Rotation-Aware Binary Projections for Efficient News Retrieval at NDTV (2026, unknown): “A parallel line learns binary codes throughsuperviseddeepnetworks,spanningpairwise[16],continuation-based[1],andvariationalorcontrastiveobjectives[2,11]([3,17] forsurveys).”