Learning to Link Entities with Knowledge Base
Date
2010-06
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Abstract
This paper address the problem of entity linking.
Specifically, given an entity mentioned in
unstructured texts, the task is to link this entity
with an entry stored in the existing knowledge
base. This is an important task for information
extraction. It can serve as a convenient
gateway to encyclopedic information, and can
greatly improve the web users’ experience.
Previous learning based solutions mainly focus
on classification framework. However, it’s
more suitable to consider it as a ranking problem.
In this paper, we propose a learning to
rank algorithm for entity linking. It effectively
utilizes the relationship information among
the candidates when ranking. The experiment
results on the TAC 20091 dataset demonstrate
the effectiveness of our proposed framework.
The proposed method achieves 18.5%
improvement in terms of accuracy over the
classification models for those entities which
have corresponding entries in the Knowledge
Base. The overall performance of the system
is also better than that of the state-of-the-art
methods.
Description
Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the ACL
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Conference Paper
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Keywords
CROSSLINKING, ENTITY LINKING, RANKING, MACHINE LEARNING, ALGORITHMS
Citation
Zheng, Z., Li, F., Huang, M., & Zhu, X. (2010). Learning to Link Entities with Knowledge Base. Proceedings of the Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the ACL, Los Angeles, USA. (p. 483-491). Association for Computational Linguistics.