Abstract
Short text classification has found rich and critical applications in news and tweet tagging to help users find relevant information. Due to lack of labeled training data in many practical use cases, there is a pressing need for studying semi-supervised short text classification. Most existing studies focus on long texts and achieve unsatisfactory performance on short texts due to the sparsity and limited labeled data. In this paper, we propose a novel heterogeneous graph neural network based method for semi-supervised short text classification, leveraging full advantage of few labeled data and large unlabeled data through information propagation along the graph. In particular, we first present a flexible HIN (heterogeneous information network) framework for modeling the short texts, which can integrate any type of additional information as well as capture their relations to address the semantic sparsity. Then, we propose Heterogeneous Graph ATtention networks (HGAT) to embed the HIN for short text classification based on a dual-level attention mechanism, including node-level and type-level attentions. The attention mechanism can learn the importance of different neighboring nodes as well as the importance of different node (information) types to a current node. Extensive experimental results have demonstrated that our proposed model outperforms state-of-the-art methods across six benchmark datasets significantly.
Highlights
With the rapid development of online social media and e-commerce, short texts, such as online news, queries, reviews, tweets, are increasingly widespread on the Internet (Song et al, 2014)
2) We propose novel heterogeneous graph attention networks (HGAT) for the HIN embedding based on a new dual-level attention mechanism which can learn the importance of different neighboring nodes and the importance of different node types to a current node
We can see that our methods significantly outperform all the baselines by a large margin, which shows the effectiveness of our proposed method on semisupervised short text classification
Summary
With the rapid development of online social media and e-commerce, short texts, such as online news, queries, reviews, tweets, are increasingly widespread on the Internet (Song et al, 2014). We propose a novel heterogeneous graph neural network based method for semisupervised short text classification, which makes full use of both limited labeled data and large unlabeled data by allowing information propagation through our automatically constructed graph. The main contributions of this paper can be summarized as follows: 1) To the best of our knowledge, this is the first attempt to model short texts as well as additional information with an HIN and adapt graph neural networks on the HIN for semi-supervised classification. 2) We propose novel heterogeneous graph attention networks (HGAT) for the HIN embedding based on a new dual-level attention mechanism which can learn the importance of different neighboring nodes and the importance of different node (information) types to a current node. 3) Extensive experimental results have demonstrated that our proposed HGAT model significantly outperforms seven state-of-the-art methods across six benchmark datasets
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