Short text clustering has far-reaching effects on semantic analysis, showing its importance for multiple applications such as corpus summarization and information retrieval. However, it inevitably encounters the severe sparsity of short text representation , making the previous clustering approaches still far from satisfactory. In this paper, we present a novel attentive representation learning model for shot text clustering, wherein cluster-level attention is proposed to capture the correlation between text representation and cluster representation . Relying on this, the representation learning and clustering for short text are seamlessly integrated into a unified framework . To further facilitate the model training process , we apply adversarial training to the unsupervised clustering setting, by adding perturbations to the cluster representations. The model parameters and perturbations are optimized alternately through a minimax game. Extensive experiments on three real-world short text datasets demonstrate the superiority of the proposed model over several strong competitors, verifying that  adversarial training yields a substantial performance gain .
Short text clustering has far-reaching effects on semantic analysis, showing its importance for multiple applications such as corpus summarization and information retrieval. However, it inevitably encounters the severe sparsity of short text representations , making the previous clustering approaches still far from satisfactory. In this paper, we present a novel attentive representation learning model for shot text clustering, wherein cluster-level attention is proposed to capture the correlations between text representations and cluster representations . Relying on this, the representation learning and clustering for short texts are seamlessly integrated into a unified model . To further ensure robust model training for short texts , we apply adversarial training to the unsupervised clustering setting, by injecting perturbations into the cluster representations. The model parameters and perturbations are optimized alternately through a minimax game. Extensive experiments on four real-world short text datasets demonstrate the superiority of the proposed model over several strong competitors, verifying that robust adversarial training yields substantial performance gains .
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representation
representations
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correlation between text representation and cluster representation
correlations between text representations and cluster representations
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text
texts
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framework
model
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facilitate the model training process
ensure robust model training for short texts
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adding perturbations to
injecting perturbations into
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three
four
A
robust
R
a substantial performance gain
substantial performance gains