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

TL;DR

This paper introduces a framework that embeds entities and hierarchical categories into a semantic space, leveraging structured knowledge and taxonomy hierarchies to improve concept categorization and dataless classification.

Contribution

It presents a novel joint embedding method that incorporates taxonomy hierarchies into entity representations for enhanced semantic relatedness and classification performance.

Findings

Superior performance on concept categorization tasks

State-of-the-art results in dataless hierarchical classification

Effective handling of both single-word and multi-word concepts

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.