@inproceedings{li-etal-2016-joint,
title = "Joint Embedding of Hierarchical Categories and Entities for Concept Categorization and Dataless Classification",
author = "Li, Yuezhang and
Zheng, Ronghuo and
Tian, Tian and
Hu, Zhiting and
Iyer, Rahul and
Sycara, Katia",
editor = "Matsumoto, Yuji and
Prasad, Rashmi",
booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
month = dec,
year = "2016",
address = "Osaka, Japan",
publisher = "The COLING 2016 Organizing Committee",
url = "https://aclanthology.org/C16-1252/",
pages = "2678--2688",
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."
}
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<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.</abstract>
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%0 Conference Proceedings
%T Joint Embedding of Hierarchical Categories and Entities for Concept Categorization and Dataless Classification
%A Li, Yuezhang
%A Zheng, Ronghuo
%A Tian, Tian
%A Hu, Zhiting
%A Iyer, Rahul
%A Sycara, Katia
%Y Matsumoto, Yuji
%Y Prasad, Rashmi
%S Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F li-etal-2016-joint
%X 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.
%U https://aclanthology.org/C16-1252/
%P 2678-2688
Markdown (Informal)
[Joint Embedding of Hierarchical Categories and Entities for Concept Categorization and Dataless Classification](https://aclanthology.org/C16-1252/) (Li et al., COLING 2016)
ACL