Text compression
Studies in part of speech labelling
HLT '91 Proceedings of the workshop on Speech and Natural Language
A maximum entropy approach to named entity recognition
A maximum entropy approach to named entity recognition
The Journal of Machine Learning Research
Named entity extraction from noisy input: speech and OCR
ANLC '00 Proceedings of the sixth conference on Applied natural language processing
A statistical profile of the Named Entity task
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
Overview of results of the MUC-6 evaluation
MUC6 '95 Proceedings of the 6th conference on Message understanding
Scaling to very very large corpora for natural language disambiguation
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
Web-a-where: geotagging web content
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
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
Language independent NER using a maximum entropy tagger
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Meta-learning orthographic and contextual models for language independent named entity recognition
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
STEWARD: architecture of a spatio-textual search engine
Proceedings of the 15th annual ACM international symposium on Advances in geographic information systems
Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems
| Hi-index | 0.00 |
Named entity recognition (NER) is a subtask of widely-recognized utility of information extraction (IE). NER has been explored in depth to provide rapid characterization of newswire data (Sundheim, 1995; Palmer and Day, 1997). The NER task involves both identification of spans of text referring to named entities, and categorization of these entities into classes based on the role they fill in context. The sentence "Washington announced that Washington ate seven hotdogs in Washington" provides an example in which a single name can arguably refer to three different entities: an organization, a person, and a location.