Understanding short texts through semantic enrichment and hashing

Abstract

Clustering short texts by their meaning is a challenging task. The semantic hashing approach encodes the meaning of a text into a compact binary code. Thus, to tell if two texts have similar meanings, we only need to check if they have similar codes. The encoding is created by a deep neural network, which is trained on texts represented by word-count vectors. Unfortunately, for short texts such as search queries, such representations are insufficient to capture the underlying semantics. We propose a method to add more semantic signals to enrich short texts. Furthermore, we introduce a simplified deep learning network constructed by stacked auto-encoders to do semantic hashing. Experiments show that our method significantly improves the understanding of short texts, including text retrieval, classification and other general text-related tasks.