Knowledge-Enhanced Named Entity Disambiguation for Short Text

Zhifan Feng, Qi Wang, Wenbin Jiang, Yajuan Lyu, Yong Zhu

Abstract
Named entity disambiguation is an important task that plays the role of bridge between text and knowledge. However, the performance of existing methods drops dramatically for short text, which is widely used in actual application scenarios, such as information retrieval and question answering. In this work, we propose a novel knowledge-enhanced method for named entity disambiguation. Considering the problem of information ambiguity and incompleteness for short text, two kinds of knowledge, factual knowledge graph and conceptual knowledge graph, are introduced to provide additional knowledge for the semantic matching between candidate entity and mention context. Our proposed method achieves significant improvement over previous methods on a large manually annotated short-text dataset, and also achieves the state-of-the-art on three standard datasets. The short-text dataset and the proposed model will be publicly available for research use.
Anthology ID:
2020.aacl-main.74
Volume:
Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing
Month:
December
Year:
2020
Address:
Suzhou, China
Editors:
Kam-Fai Wong, Kevin Knight, Hua Wu
Venue:
AACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
735–744
Language:
URL:
https://aclanthology.org/2020.aacl-main.74
DOI:
Bibkey:
Cite (ACL):
Zhifan Feng, Qi Wang, Wenbin Jiang, Yajuan Lyu, and Yong Zhu. 2020. Knowledge-Enhanced Named Entity Disambiguation for Short Text. In Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing, pages 735–744, Suzhou, China. Association for Computational Linguistics.
Cite (Informal):
Knowledge-Enhanced Named Entity Disambiguation for Short Text (Feng et al., AACL 2020)
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