Entity understanding with hierarchical graph learning for enhanced text classification

摘要

Text classification plays an important role in the areas of natural language processing and data mining. In general, a text is usually described around a collection of entities, i.e., the entities are the core part of the text. As a result, a deep understanding of the entities in a text benefits the classification of texts. To understand entities, traditional work tends to introduce concepts or web data for entities. However, we argue that the potential relations between entities are also important for the understanding of entity semantics, thus further supporting the classification of texts. In this paper, we focus on enhancing the performance of the existing text classification models by extracting features from entities with hierarchical graph learning. To this end, we mine the concepts of entities and the relations between them for a given text simultaneously, and further construct the semantic graph of the text. Then a novel hierarchical graph learning model is proposed to learn the graph embedding that well captures the node, relation, and graph structure information. Our experiments show that the proposed method has the ability to effectively improve the performance of the existing text classifiers.