Query expansion using local and global document analysis
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
A Markov random field model for term dependencies
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Latent concept expansion using markov random fields
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Overview of INEX 2007 Link the Wiki Track
Focused Access to XML Documents
Collective annotation of Wikipedia entities in web text
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning
Knowledge base population: successful approaches and challenges
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
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
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
Constructing query-specific knowledge bases
Proceedings of the 2013 workshop on Automated knowledge base construction
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Entity Linking is the task of mapping a string in a document to its entity in a knowledge base. One of the crucial tasks is to identify disambiguating context; joint assignment models leverage the relationships within the knowledge base. We demonstrate how joint assignment models can be approximated with information retrieval. We introduce the neighborhood relevance model which uses relevance feedback techniques to identify the salience of entity context using cross-document evidence. We show that this model is more effective than local document models for ranking KB entities. Experiments on the TAC KBP entity linking task demonstrate that our model is the best performing system for strings that are linkable to the knowledge base.