Active Hidden Markov Models for Information Extraction
IDA '01 Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis
A maximum entropy approach to named entity recognition
A maximum entropy approach to named entity recognition
Nymble: a high-performance learning name-finder
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
Named entity recognition: a maximum entropy approach using global information
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Ranking algorithms for named-entity extraction: boosting and the voted perceptron
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
The N-Best algorithm: an efficient procedure for finding top N sentence hypotheses
HLT '89 Proceedings of the workshop on Speech and Natural Language
ACLdemo '04 Proceedings of the ACL 2004 on Interactive poster and demonstration sessions
Using N-best lists for named entity recognition from Chinese speech
HLT-NAACL-Short '04 Proceedings of HLT-NAACL 2004: Short Papers
Redundancy-based correction of automatically extracted facts
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Analysis and repair of name tagger errors
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
Look who is talking: soundbite speaker name recognition in broadcast news speech
NAACL-Short '07 Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Companion Volume, Short Papers
Entity extraction is a boring solved problem: or is it?
NAACL-Short '07 Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Companion Volume, 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
Data selection in semi-supervised learning for name tagging
IEBeyondDoc '06 Proceedings of the Workshop on Information Extraction Beyond The Document
Arabic cross-document person name normalization
Semitic '07 Proceedings of the 2007 Workshop on Computational Approaches to Semitic Languages: Common Issues and Resources
Identification of Soundbite and Its Speaker Name Using Transcripts of Broadcast News Speech
ACM Transactions on Asian Language Information Processing (TALIP)
Semi-joint labeling for chinese named entity recognition
AIRS'08 Proceedings of the 4th Asia information retrieval conference on Information retrieval technology
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Information extraction systems incorporate multiple stages of linguistic analysis. Although errors are typically compounded from stage to stage, it is possible to reduce the errors in one stage by harnessing the results of the other stages. We demonstrate this by using the results of coreference analysis and relation extraction to reduce the errors produced by a Chinese name tagger. We use an N-best approach to generate multiple hypotheses and have them re-ranked by subsequent stages of processing. We obtained thereby a reduction of 24% in spurious and incorrect name tags, and a reduction of 14% in missed tags.