SIGDOC '86 Proceedings of the 5th annual international conference on Systems documentation
Introduction: named entity recognition in biomedicine
Journal of Biomedical Informatics - Special issue: Named entity recognition in biomedicine
The effect of named entities on effectiveness in cross-language information retrieval evaluation
Proceedings of the 2005 ACM symposium on Applied computing
Fine grained classification of named entities
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Introduction to the CoNLL-2000 shared task: chunking
ConLL '00 Proceedings of the 2nd workshop on Learning language in logic and the 4th conference on Computational natural language learning - Volume 7
Use of support vector learning for chunk identification
ConLL '00 Proceedings of the 2nd workshop on Learning language in logic and the 4th conference on Computational natural language learning - Volume 7
An empirical evaluation of knowledge sources and learning algorithms for word sense disambiguation
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Introduction to the CoNLL-2002 shared task: language-independent named entity recognition
COLING-02 proceedings of the 6th conference on Natural language learning - Volume 20
Maximum entropy models for named entity recognition
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Named entity recognition through classifier combination
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Names and similarities on the web: fact extraction in the fast lane
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Weakly-supervised discovery of named entities using web search queries
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
YAGO: A Large Ontology from Wikipedia and WordNet
Web Semantics: Science, Services and Agents on the World Wide Web
Fine-grained classification of named entities exploiting latent semantic kernels
CoNLL '09 Proceedings of the Thirteenth Conference on Computational Natural Language Learning
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Instance-based ontology population exploiting named-entity substitution
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Deriving a large scale taxonomy from Wikipedia
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Extended gloss overlaps as a measure of semantic relatedness
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Unsupervised named-entity extraction from the Web: An experimental study
Artificial Intelligence
GEMS '11 Proceedings of the GEMS 2011 Workshop on GEometrical Models of Natural Language Semantics
A graph-based approach for ontology population with named entities
Proceedings of the 21st ACM international conference on Information and knowledge management
Universal schema for entity type prediction
Proceedings of the 2013 workshop on Automated knowledge base construction
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Named Entity Recognition and Classification (NERC) is a well-studied NLP task typically focused on coarse-grained named entity (NE) classes. NERC for more fine-grained semantic NE classes has not been systematically studied. This paper quantifies the difficulty of fine-grained NERC (FG-NERC) when performed at large scale on the people domain. We apply unsupervised acquisition methods to construct a gold standard dataset for FG-NERC. This dataset is used to benchmark methods for classifying NEs at various levels of fine-grainedness using classical NERC techniques and global contextual information inspired from Word Sense Disambiguation approaches. Our results indicate high difficulty of the task and provide a 'strong' baseline for future research.