Improving Open Directory Project-Based Text Classification with Hierarchical Category Embedding

Many works have used knowledge bases that contain taxonomy of hierarchically structured categories for large-scale text classification. These works have utilized hierarchical taxonomies based on the explicit representation ^model. They demonstrated that the explicit representation model provides a stable performance for large-scale text classification. However, this performance is limited to the knowledge base. In this paper, we integrate the implicit representation model, which has the ability to use external knowledge indirectly, with previous large-scale text classification. To this end, we first propose Hierarchical Category embedding (HC embedding) to generate distributed representations of hierarchical categories based on the implicit representation model. Second, we develop a new semantic similarity method to integrate HC embedding with the large-scale text classification. To demonstrate efficacy, we apply the proposed methodology to Open Directory Project (ODP)-based text classification, which has a hierarchical taxonomy. The evaluation results demonstrate that the proposed method outperforms the current state-of-the-art method by 7.4 %, 7.0 %, and 18 % in terms of micro-averaging F1-score, macro-averaging F1-score, and precision at k, respectively.