KIDER: Knowledge-Infused Document Embedding Representation for Text Categorization

Advancement of deep learning has improved performances on a wide variety of tasks. However, language reasoning and understanding remain difficult tasks in Natural Language Processing (NLP). In this work, we consider this problem and propose a novel Knowledge-Infused Document Embedding Representation (KIDER) for text categorization. We use knowledge patterns to generate high quality document representation. These patterns preserve categorical-distinctive semantic information, provide interpretability, and achieve superior performances at the same time. Experiments show that the KIDER model outperforms state-of-the-art methods on two important NLP tasks, i.e., emotion analysis and news topic detection, by 7% and 20%. In addition, we also demonstrate the potential of highlighting important information for each category and news using these patterns. These results show the value of knowledge-infused patterns in terms of interpretability and performance enhancement.