Learning entity representation for entity disambiguation

ACL 2013 - 51st Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference

Volumn 2, Issue , 2013, Pages 30-34

  He, Zhengyan ^a   Liu, Shujie ^b   Li, Mu ^b   Zhou, Ming ^b   Zhang, Longkai ^a   Wang, Houfeng ^a  

^a Peking University   (China)

^b MICROSOFT RESEARCH ASIA   (China)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL LINGUISTICS;

DE-NOISING; DEEP NEURAL NETWORKS; DOCUMENT REPRESENTATION; ENTITY DISAMBIGUATION; PRE-TRAINING; SIMILARITY MEASURE; STATE-OF-THE-ART PERFORMANCE;

COMPLEX NETWORKS;

EID: 84907317782     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (21)

1. Y. Bengio, P. Lamblin, D. Popovici, and H. Larochelle. 2007. Greedy layer-wise training of deep networks. Advances in neural information processing systems, 19: 153.
2. R. Bunescu and M. Pasca. 2006. Using encyclopedic knowledge for named entity disambiguation. In Proceedings of EACL, Volume 6, pages 9-16.
3. S. Cucerzan. 2007. Large-scale named entity disambiguation based on wikipedia data. In Proceedings of EMNLP-CoNLL, Volume 6, pages 708-716.
4. Y Dauphin, X. Glorot, and Y Bengio. 2011. Large-scale learning of embeddings with reconstruction sampling. In Proceedings of the Twenty-eighth International Conference on Machine Learning (ICML11).
5. X. Glorot, A. Bordes, and Y Bengio. 2011. Domain adaptation for large-scale sentiment classification: A deep learning approach. In Proceedings of the 28th International Conference on Machine Learning.
6. Christoph Goller and Andreas Kuchler. 1996. Learning task-dependent distributed representations by backpropagation through structure. In Neural Networks, 1996., IEEE International Conference on, Volume 1, pages 347-352. IEEE.
7. X. Han, L. Sun, and J. Zhao. 2011. Collective entity linking in web text: a graph-based method. In Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval, pages 765-774. ACM.
8. GE. Hinton, S. Osindero, and YW. Teh. 2006. A fast learning algorithm for deep belief nets. Neural computation, 18(7):1527-1554.
9. J. Hoffart, M.A. Yosef, I. Bordino, H. Fürstenau, M. Pinkal, M. Spaniol, B. Taneva, S. Thater, and G Weikum. 2011. Robust disambiguation of named entities in text. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, pages 782-792. Association for Computational Linguistics.
10. Heng Ji and Ralph Grishman. 2011. Knowledge base population: Successful approaches and challenges. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pages 1148-1158, Portland, Oregon, USA, June. Association for Computational Linguistics.
11. S.S. Kataria, K.S. Kumar, R. Rastogi, P. Sen, and S.H. Sengamedu. 2011. Entity disambiguation with hierarchical topic models. In Proceedings of KDD.
12. S. Kulkarni, A. Singh, G. Ramakrishnan, and S. Chakrabarti. 2009. Collective annotation of wikipedia entities in web text. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 457-466. ACM.
13. J. Lehmann, S. Monahan, L. Nezda, A. Jung, and Y. Shi. 2010. Lcc approaches to knowledge base population at tac 2010. In Proc. TAC 2010 Workshop.
14. L. Ratinov, D. Roth, D. Downey, and M. Anderson. 2011. Local and global algorithms for disambiguation to wikipedia. In Proceedings of the Annual Meeting of the Association of Computational Linguistics (ACL).
15. P. Sen. 2012. Collective context-aware topic models for entity disambiguation. In Proceedings of the 21st international conference on World Wide Web, pages 729-738. ACM.
16. M. Shirakawa, H. Wang, Y. Song, Z. Wang, K. Nakayama, T. Hara, and S. Nishio. 2011. Entity disambiguation based on a probabilistic taxonomy. Technical report, Technical Report MSR-TR-2011-125, Microsoft Research.
17. N.A. Smith and J. Eisner. 2005. Contrastive estimation: Training log-linear models on unlabeled data. In Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics, pages 354-362. Association for Computational Linguistics.
18. P. Vincent, H. Larochelle, Y. Bengio, and P.A. Manzagol. 2008. Extracting and composing robust features with denoising autoencoders. In Proceedings of the 25th international conference on Machine learning, pages 1096-1103. ACM.
19. Pascal Vincent, Hugo Larochelle, Isabelle Lajoie, Yoshua Bengio, and Pierre-Antoine Manzagol. 2010. Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion. The Journal of Machine Learning Research, 11:3371-3408.
20. W. Zhang, Y.C. Sim, J. Su, and C.L. Tan. 2011. Entity linking with effective acronym expansion, instance selection and topic modeling. In Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three, pages 1909-1914. AAAI Press.
21. 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, Los Angeles, California, June. Association for Computational Linguistics.