Ronghuo Zheng


Joint Embedding of Hierarchical Categories and Entities for Concept Categorization and Dataless Classification
Yuezhang Li | Ronghuo Zheng | Tian Tian | Zhiting Hu | Rahul Iyer | Katia Sycara
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

Existing work learning distributed representations of knowledge base entities has largely failed to incorporate rich categorical structure, and is unable to induce category representations. We propose a new framework that embeds entities and categories jointly into a semantic space, by integrating structured knowledge and taxonomy hierarchy from large knowledge bases. Our framework enables to compute meaningful semantic relatedness between entities and categories in a principled way, and can handle both single-word and multiple-word concepts. Our method shows significant improvement on the tasks of concept categorization and dataless hierarchical classification.