Abstract. Everyday, millions of short-texts are generated for which effective tools for organization and retrieval are required. Because of the tiny length of these documents and of their extremely sparse representations, the direct application of standard text categorization methods is not effective. In this work we propose using distributional term representations (DTRs) for short-text categorization. DTRs represent terms by means of contextual information, given by document occurrence and term co-occurrence statistics. Therefore, they allow us to develop enriched document representations that help to overcome, to some extent, the small-length and high-sparsity issues. We report experimental results in three challenging collections, using a variety of classification methods. These results show that the use of DTRs is beneficial for improving the classification performance of classifiers in short-text categorization.
Cabrera et al. REF proposed a semi-supervised short text classification method, Distributional Term Representations (DTRs), which enriched document representations based on contextual information that overcame the small-length and high-sparseness short text to some extent.
Distributional Term Representations for Short-Text Categorization
CICLing
Computer Science