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


Abstract
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.
Anthology ID:
C16-1252
Volume:
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
Month:
December
Year:
2016
Address:
Osaka, Japan
Editors:
Yuji Matsumoto, Rashmi Prasad
Venue:
COLING
SIG:
Publisher:
The COLING 2016 Organizing Committee
Note:
Pages:
2678–2688
Language:
URL:
https://aclanthology.org/C16-1252
DOI:
Bibkey:
Cite (ACL):
Yuezhang Li, Ronghuo Zheng, Tian Tian, Zhiting Hu, Rahul Iyer, and Katia Sycara. 2016. Joint Embedding of Hierarchical Categories and Entities for Concept Categorization and Dataless Classification. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 2678–2688, Osaka, Japan. The COLING 2016 Organizing Committee.
Cite (Informal):
Joint Embedding of Hierarchical Categories and Entities for Concept Categorization and Dataless Classification (Li et al., COLING 2016)
Copy Citation:
PDF:
https://aclanthology.org/C16-1252.pdf
Data
RCV1