Joint Embedding of Hierarchical Categories and Entities for Concept Categorization and Dataless Classification
This work addresses the challenge of incorporating category hierarchies into entity embeddings for researchers and practitioners in natural language processing and knowledge representation, offering an incremental improvement over existing methods.
The paper tackles the problem of learning distributed representations for categories without structured knowledge by proposing a framework that jointly embeds entities and categories using knowledge bases and taxonomy hierarchies, achieving superior performance on concept categorization and state-of-the-art results on dataless hierarchical classification.
Due to the lack of structured knowledge applied in learning distributed representation of cate- gories, existing work cannot incorporate category hierarchies into entity information. We propose a framework that embeds entities and categories into a semantic space by integrating structured knowledge and taxonomy hierarchy from large knowledge bases. The framework allows to com- pute meaningful semantic relatedness between entities and categories. Our framework can han- dle both single-word concepts and multiple-word concepts with superior performance on concept categorization and yield state of the art results on dataless hierarchical classification.