Learning dictionaries for information extraction by multi-level bootstrapping
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Acquisition of categorized named entities for web search
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Unsupervised learning of name structure from coreference data
NAACL '01 Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies
Unsupervised named-entity extraction from the web: an experimental study
Artificial Intelligence
Introduction to the CoNLL-2003 shared task: language-independent named entity recognition
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Coarse-to-fine n-best parsing and MaxEnt discriminative reranking
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
A permutation-augmented sampler for DP mixture models
Proceedings of the 24th international conference on Machine learning
Identification and tracing of ambiguous names: discriminative and generative approaches
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
EM works for pronoun anaphora resolution
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
Unsupervised models for coreference resolution
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Coreference resolution in a modular, entity-centered model
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Variational inference for adaptor grammars
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Type level clustering evaluation: new measures and a POS induction case study
CoNLL '10 Proceedings of the Fourteenth Conference on Computational Natural Language Learning
Bootstrapping coreference resolution using word associations
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Producing Power-Law Distributions and Damping Word Frequencies with Two-Stage Language Models
The Journal of Machine Learning Research
MWE '11 Proceedings of the Workshop on Multiword Expressions: from Parsing and Generation to the Real World
Structured databases of named entities from Bayesian nonparametrics
EMNLP '11 Proceedings of the First Workshop on Unsupervised Learning in NLP
Named entity recognition in tweets: an experimental study
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Entity clustering across languages
NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
A probabilistic model for canonicalizing named entity mentions
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
Universal schema for entity type prediction
Proceedings of the 2013 workshop on Automated knowledge base construction
Unsupervised biomedical named entity recognition: Experiments with clinical and biological texts
Journal of Biomedical Informatics
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We describe a generative model for clustering named entities which also models named entity internal structure, clustering related words by role. The model is entirely unsupervised; it uses features from the named entity itself and its syntactic context, and coreference information from an unsupervised pronoun resolver. The model scores 86% on the MUC-7 named-entity dataset. To our knowledge, this is the best reported score for a fully unsupervised model, and the best score for a generative model.