Joint Embedding of Hierarchical Categories and Entities for Concept Categorization and Dataless Classification

Abstract

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.

10 pages, submitted to Coling 2016. arXiv admin note: substantial text overlap with arXiv:1605.03924

References in corpus (2)

• Natural Language Processing (almost) from Scratch
• Inferring Missing Entity Type Instances for Knowledge Base Completion: New Dataset and Methods

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