Learning Short Text Representation using NonNegative Matrix Factorization and Word Semantic Correlations With the emergence of online medias, short texts have been increasingly available. Because the short texts have a limited contextual information, they are sparse, noisy and ambiguous. Conventional models to text representation are hence not applicable. In this paper, we propose a representation learning model for short texts to tackle this challenge. Our proposed model is based on NonNegative Matrix Factorization (NNMF). Due to the problem lacking word cooccurrences information, it effectively learns the representation model by incorporating the semantic relationship between words generated by unsupervised learning methods. Finally, the NNMFbased model is effectively solved by using a gradient descentbased algorithm. Experimental results on two benchmark data sets, including sentiment140 and 20newsgroups, demonstrate that our proposed approach achieved the improvement of classification accuracy to stateoftheart topic discovery techniques.

Short Text Embedding for Clustering Based on Word and Topic Semantic Information Short text clustering is used in various applications and becomes a significant problem, while it also is a challenging task due to the sparsity problem of traditional short text representations. Early methods either cause waste of space or ignore the order of word sequence. To tackle these problems, a selftaught convolutional neural network model is proposed to construct short text representations. However, it extracts the semantic information only from the word context without any other unsupervised features and ignores the different contributions of textual content in clustering. In this paper, we propose an effective short text embedding method for clustering based on word and topic semantic information (STEWT). Taking advantage of the topic semantic information and capturing the differences in the contributions of the content by an attention mechanism, our proposed model successfully constructs much better short text representations for clustering. Extensive experimental results on real datasets demonstrate the effectiveness and superiority of our framework compared with stateoftheart methods.