Learning to Link Entities with Knowledge Base

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...

• Type: Journal
• Year: 2010
• Where: NAACL
• Authors: Zhicheng Zheng, Fangtao Li, Minlie Huang, Xiaoyan Zhu
• Keywords: Computational Linguistics, Knowledge Base, NAACL 2010, TAC 20091 Dataset, Unstructured Texts