The Journal of Machine Learning Research
The mathematics of statistical machine translation: parameter estimation
Computational Linguistics - Special issue on using large corpora: II
Discovering missing links in Wikipedia
Proceedings of the 3rd international workshop on Link discovery
Mining Domain-Specific Thesauri from Wikipedia: A Case Study
WI '06 Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence
Wikify!: linking documents to encyclopedic knowledge
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Learning to link with wikipedia
Proceedings of the 17th ACM conference on Information and knowledge management
Collective annotation of Wikipedia entities in web text
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Identification and tracing of ambiguous names: discriminative and generative approaches
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Learning to link entities with knowledge base
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Entity disambiguation for knowledge base population
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Entity linking leveraging: automatically generated annotation
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Resolving surface forms to Wikipedia topics
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
A generative entity-mention model for linking entities with knowledge base
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Local and global algorithms for disambiguation to Wikipedia
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Collective entity linking in web text: a graph-based method
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Entity disambiguation with hierarchical topic models
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Name discrimination by clustering similar contexts
CICLing'05 Proceedings of the 6th international conference on Computational Linguistics and Intelligent Text Processing
Collaborative ranking: a case study on entity linking
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Robust disambiguation of named entities in text
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Linking entities to a knowledge base with query expansion
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Collective context-aware topic models for entity disambiguation
Proceedings of the 21st international conference on World Wide Web
Entity linking with effective acronym expansion, instance selection and topic modeling
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Mining evidences for named entity disambiguation
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
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Entity Linking (EL) has received considerable attention in recent years. Given many name mentions in a document, the goal of EL is to predict their referent entities in a knowledge base. Traditionally, there have been two distinct directions of EL research: one focusing on the effects of mention's context compatibility, assuming that "the referent entity of a mention is reflected by its context"; the other dealing with the effects of document's topic coherence, assuming that "a mention's referent entity should be coherent with the document's main topics". In this paper, we propose a generative model -- called entity-topic model, to effectively join the above two complementary directions together. By jointly modeling and exploiting the context compatibility, the topic coherence and the correlation between them, our model can accurately link all mentions in a document using both the local information (including the words and the mentions in a document) and the global knowledge (including the topic knowledge, the entity context knowledge and the entity name knowledge). Experimental results demonstrate the effectiveness of the proposed model.