emnlp emnlp2011 emnlp2011-90 emnlp2011-90-reference knowledge-graph by maker-knowledge-mining
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Author: Swapna Gottipati ; Jing Jiang
Abstract: In this paper we present a novel approach to entity linking based on a statistical language model-based information retrieval with query expansion. We use both local contexts and global world knowledge to expand query language models. We place a strong emphasis on named entities in the local contexts and explore a positional language model to weigh them differently based on their distances to the query. Our experiments on the TAC-KBP 2010 data show that incorporating such contextual information indeed aids in disambiguating the named entities and consistently improves the entity linking performance. Compared with the official results from KBP 2010 participants, our system shows competitive performance.
Razvan Bunescu and Marius Pasca. 2006. Using encyclopedic knowledge for named entity disambiguation. In Proceesings of the 11th Conference of the European Chapter of the Association for Computational Linguistics (EACL-06), pages 9–16, Trento, Italy. Silviu Cucerzan. 2007. Large-scale named entity disambiguation based on Wikipedia data. In Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pages 708–716. Mark Dredze, Paul McNamee, Delip Rao, Adam Ger- ber, and Tim Finin. 2010. Entity disambiguation for knowledge base population. In Proceedings of the 23rd International Conference on Computational Linguistics, pages 277–285. Heng Ji, Ralph Grishman, Hoa Trang Dang, Kira Griffitt, and Joe Ellis. 2010. Overview of the TAC 2010 knowledge base population track. In Proceedings of the Third Text Analysis Conference. 813 John Lafferty and ChengXiang Zhai. 2001. Document language models, query models, and risk minimization for information retrieval. In Proceedings of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 111–1 19. Victor Lavrenko and W. Bruce Croft. 2001. Relevance based language models. In Proceedings of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 120–127. John Lehmann, Sean Monahan, Luke Nezda, Arnold Jung, and Ying Shi. 2010. Lcc approaches to knowledge base population at tac 2010. In Proceedings TAC 2010 Workshop. TAC 2010. Yuanhua Lv and ChengXiang Zhai. 2009. A comparative study of methods for estimating query language models with pseudo feedback. In Proceeding of the 18th ACM Conference on Information and Knowledge Management, pages 1895–1898. Yuanhua Lv and ChengXiang Zhai. 2010. Positional relevance model for pseudo-relevance feedback. In Proceeding of the 33rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 579–586. Paul McNamee and Hoa Trang Dang. 2009. Overview of the TAC 2009 knowledge base population track. In Proceedings of the Second Text Analysis Conference. ChengXiang Zhai and John Lafferty. 2001. Model-based feedback in the language modeling approach to information retrieval. In Proceedings of the 10th International Conference on Information and Knowledge Management, pages 403–410. Chengxiang Zhai and John Lafferty. 2004. A study of smoothing methods for language models applied to information retrieval. ACM Transactions on Information Systems, 22(2): 179–214, April. Wei Zhang, Jian Su, Chew Lim Tan, and Wen Ting Wang. 2010. Entity linking leveraging automatically generated annotation. In Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010), pages 1290–1298. Zhicheng Zheng, Fangtao Li, Minlie Huang, and Xiaoyan Zhu. 2010. Learning to link entities with knowledge base. In Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, pages 483–491.