Special Section on Recent Advances in Machine Learning for Spoken Language Processing
Short Text Classification Based on Distributional Representations of Words
2016 Volume E99.D Issue 10 Pages 2562-2565
Details
2016 Volume E99.D Issue 10 Pages 2562-2565
Short texts usually encounter the problem of data sparseness, as they do not provide sufficient term co-occurrence information. In this paper, we show how to mitigate the problem in short text classification through word embeddings. We assume that a short text document is a specific sample of one distribution in a Gaussian-Bayesian framework. Furthermore, a fast clustering algorithm is utilized to expand and enrich the context of short text in embedding space. This approach is compared with those based on the classical bag-of-words approaches and neural network based methods. Experimental results validate the effectiveness of the proposed method.
