Learning to classify short text from scientific documents using topic models with various types of knowledge

An efficient framework to classify short text from scientific documents is proposed.Topic models from various types of knowledge were used for enhancing features in documents.Two methods were presented to optimize external features that enhance relatedness in documents.The performances were evaluated by using real-world scientific documents from online publisher.Proposed methods are shown to outperform related work. Classification of short text is challenging due to data sparseness, which is a typical characteristic of short text. In this paper, we propose methods for enhancing features using topic models, which make short text seem less sparse and more topic-oriented for classification. We exploited topic model analysis based on Latent Dirichlet Allocation for enriched datasets, and then we presented new methods for enhancing features by combining external texts from topic models that make documents more effective for classification. In experiments, we utilized the title contents of scientific articles as short text documents, and then enriched these documents using topic models from various types of universal datasets for classification in order to show that our approach performs efficiently.