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 …

Short text classification has been widely explored in news tagging to provide more efficient
search strategies and more effective search results for information retrieval. However, most …

In real-world scenarios, considerable human power and expert knowledge are required to
label data. Therefore, solving short text classification problems in a semi-supervised manner …

Short text classification is a fundamental task in natural language processing. It is hard due
to the lack of context information and labeled data in practice. In this paper, we propose a …

The short text, sparse features, and the lack of training data, etc. are still the key bottlenecks
that restrict the successful application of traditional text classification methods. To address …

Short text classification is one of important tasks in Natural Language Processing (NLP).
Unlike paragraphs or documents, short texts are more ambiguous since they have not …

Semi-supervised short text classification is a challenging problem due to the sparsity and
limited labeled data. Due to the lack of labeled data, many models focus on the generation …

Short text classification is a challenging task in natural language processing. Existing
traditional methods using external knowledge to deal with the sparsity and ambiguity of short …

Many classification models work poorly on short texts due to data sparsity. To address this
issue, we propose topic memory networks for short text classification with a novel topic …

A semantic extension-based classification algorithm for short texts, ie, Set-CNN, is proposed
in this paper. The proposed Set-CNN features three aspects. First, a semantic extension …