Ye, F;
Manotumruksa, J;
Yilmaz, E;
(2020)
Unsupervised Few-Bits Semantic Hashing with Implicit Topics Modeling.
In: Cohn, T and He, Y and Liu, Y, (eds.)
Findings of the Association for Computational Linguistics: EMNLP 2020.
(pp. pp. 2566-2575).
Association for Computational Linguistics (ACL): Online conference.
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Abstract
Semantic hashing is a powerful paradigm for representing texts as compact binary hash codes. The explosion of short text data has spurred the demand of few-bits hashing. However, the performance of existing semantic hashing methods cannot be guaranteed when applied to few-bits hashing because of severe information loss. In this paper, we present a simple but effective unsupervised neural generative semantic hashing method with a focus on few-bits hashing. Our model is built upon variational autoencoder and represents each hash bit as a Bernoulli variable, which allows the model to be end-to-end trainable. To address the issue of information loss, we introduce a set of auxiliary implicit topic vectors. With the aid of these topic vectors, the generated hash codes are not only low-dimensional representations of the original texts but also capture their implicit topics. We conduct comprehensive experiments on four datasets. The results demonstrate that our approach achieves significant improvements over state-of-the-art semantic hashing methods in few-bits hashing.
| Type: | Proceedings paper |
|---|---|
| Title: | Unsupervised Few-Bits Semantic Hashing with Implicit Topics Modeling |
| Event: | 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.18653/v1/2020.findings-emnlp.233 |
| Publisher version: | http://dx.doi.org/10.18653/v1/2020.findings-emnlp.... |
| Language: | English |
| Additional information: | This is an Open Access paper published under a Creative Commons Attribution 4.0 International (CC BY 4.0) licence (https://creativecommons.org/licenses/by/4.0/). |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10117563 |
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