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.- Anthology ID:
- 2020.findings-emnlp.233
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2020
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Editors:
- Trevor Cohn, Yulan He, Yang Liu
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2566–2575
- Language:
- URL:
- https://aclanthology.org/2020.findings-emnlp.233
- DOI:
- 10.18653/v1/2020.findings-emnlp.233
- Cite (ACL):
- Fanghua Ye, Jarana Manotumruksa, and Emine Yilmaz. 2020. Unsupervised Few-Bits Semantic Hashing with Implicit Topics Modeling. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 2566–2575, Online. Association for Computational Linguistics.
- Cite (Informal):
- Unsupervised Few-Bits Semantic Hashing with Implicit Topics Modeling (Ye et al., Findings 2020)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2020.findings-emnlp.233.pdf
- Code
- smartyfh/wish