Papers by Houye Ji
Heterogeneous Graph Attention Networks for Semi-supervised Short Text Classification (D19-1)
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| Challenge: | Existing studies on short text classification focus on long texts and achieve unsatisfactory performance due to the sparsity and limited labeled data. |
| Approach: | They propose a heterogeneous graph neural network based method for semi-supervised short text classification that leverages the full advantage of few labeled data and large unlabeled data through information propagation along the graph. |
| Outcome: | The proposed method outperforms state-of-the-art methods across six benchmark datasets significantly. |