  Due to computational and storage efficiencies of compact binary codes,
hashing has been widely used for large-scale similarity search. Unfortunately,
many existing hashing methods based on observed keyword features are not
effective for short texts due to the sparseness and shortness. Recently, some
researchers try to utilize latent topics of certain granularity to preserve
semantic similarity in hash codes beyond keyword matching. However, topics of
certain granularity are not adequate to represent the intrinsic semantic
information. In this paper, we present a novel unified approach for short text
Hashing using Multi-granularity Topics and Tags, dubbed HMTT. In particular, we
propose a selection method to choose the optimal multi-granularity topics
depending on the type of dataset, and design two distinct hashing strategies to
incorporate multi-granularity topics. We also propose a simple and effective
method to exploit tags to enhance the similarity of related texts. We carry out
extensive experiments on one short text dataset as well as on one normal text
dataset. The results demonstrate that our approach is effective and
significantly outperforms baselines on several evaluation metrics.
