E ectively making sense of short texts is a critical task for many real world applications such as search engines, social media services, and recommender systems. e task is particularly challenging as a short text contains very sparse information, o en too sparse for a machine learning algorithm to pick up useful signals. A common practice for analyzing short text is to rst expand it with external information, which is usually harvested from a large collection of longer texts. In literature, short text expansion has been done with all kinds of heuristics. We propose an end-to-end solution that automatically learns how to expand short text to optimize a given learning task. A novel deep memory network is proposed to automatically nd relevant information from a collection of longer documents and reformulate the short text through a gating mechanism. Using short text classi cation as a demonstrating task, we show that the deep memory network signi cantly outperforms classical text expansion methods with comprehensive experiments on real world data sets.
Tang et al. REF proposed an end-to-end solution to the short text sparseness and automatically learned how to extend the short text to optimize a given learning task.
End-to-end Learning for Short Text Expansion
KDD '17
Computer Science