An Efficient Boosting Algorithm for Combining Preferences
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An entity-topic model for entity linking
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
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WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
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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 2009 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.