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
Structural semantic relatedness: a knowledge-based method to named entity disambiguation
ACL '10 Proceedings of the 48th Annual Meeting 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
Name discrimination by clustering similar contexts
CICLing'05 Proceedings of the 6th international conference on Computational Linguistics and Intelligent Text Processing
LIEGE:: link entities in web lists with knowledge base
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
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
A context-aware approach to entity linking
AKBC-WEKEX '12 Proceedings of the Joint Workshop on Automatic Knowledge Base Construction and Web-scale Knowledge Extraction
Evaluating Entity Linking with Wikipedia
Artificial Intelligence
Mining evidences for named entity disambiguation
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Improving efficiency and accuracy in multilingual entity extraction
Proceedings of the 9th International Conference on Semantic Systems
Discovering links between political debates and media
ICWE'13 Proceedings of the 13th international conference on Web Engineering
Cross lingual entity linking with bilingual topic model
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Entity linking at the tail: sparse signals, unknown entities, and phrase models
Proceedings of the 7th ACM international conference on Web search and data mining
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Linking entities with knowledge base (entity linking) is a key issue in bridging the textual data with the structural knowledge base. Due to the name variation problem and the name ambiguity problem, the entity linking decisions are critically depending on the heterogenous knowledge of entities. In this paper, we propose a generative probabilistic model, called entity-mention model, which can leverage heterogenous entity knowledge (including popularity knowledge, name knowledge and context knowledge) for the entity linking task. In our model, each name mention to be linked is modeled as a sample generated through a three-step generative story, and the entity knowledge is encoded in the distribution of entities in document P(e), the distribution of possible names of a specific entity P(s\e), and the distribution of possible contexts of a specific entity P(c\e). To find the referent entity of a name mention, our method combines the evidences from all the three distributions P(e), P(s\e) and P(c\e). Experimental results show that our method can significantly outperform the traditional methods.