Nymble: a high-performance learning name-finder
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
Probabilistic reasoning for entity & relation recognition
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Chinese Word Segmentation and Named Entity Recognition: A Pragmatic Approach
Computational Linguistics
Improving name tagging by reference resolution and relation detection
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Using N-best lists for named entity recognition from Chinese speech
HLT-NAACL-Short '04 Proceedings of HLT-NAACL 2004: Short Papers
Re-ranking algorithms for name tagging
CHSLP '06 Proceedings of the Workshop on Computationally Hard Problems and Joint Inference in Speech and Language Processing
Automatic recognition of logical relations for English, Chinese and Japanese in the GLARF framework
DEW '09 Proceedings of the Workshop on Semantic Evaluations: Recent Achievements and Future Directions
Re-ranking algorithms for name tagging
CHSLP '06 Proceedings of the Workshop on Computationally Hard Problems and Joint Inference in Speech and Language Processing
Transducing logical relations from automatic and manual GLARF
ACL-IJCNLP '09 Proceedings of the Third Linguistic Annotation Workshop
On jointly recognizing and aligning bilingual named entities
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Improving MT word alignment using aligned multi-stage parses
SSST-5 Proceedings of the Fifth Workshop on Syntax, Semantics and Structure in Statistical Translation
Joint bilingual name tagging for parallel corpora
Proceedings of the 21st ACM international conference on Information and knowledge management
A joint model to identify and align bilingual named entities
Computational Linguistics
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Name tagging is a critical early stage in many natural language processing pipelines. In this paper we analyze the types of errors produced by a tagger, distinguishing name classification and various types of name identification errors. We present a joint inference model to improve Chinese name tagging by incorporating feedback from subsequent stages in an information extraction pipeline: name structure parsing, cross-document coreference, semantic relation extraction and event extraction. We show through examples and performance measurement how different stages can correct different types of errors. The resulting accuracy approaches that of individual human annotators.