Decision Combination in Multiple Classifier Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Learning to Parse Natural Language with Maximum Entropy Models
Machine Learning - Special issue on natural language learning
A maximum entropy approach to named entity recognition
A maximum entropy approach to named entity recognition
Text chunking based on a generalization of winnow
The Journal of Machine Learning Research
A machine learning approach to coreference resolution of noun phrases
Computational Linguistics - Special issue on computational anaphora resolution
Nymble: a high-performance learning name-finder
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
Classifier combination for improved lexical disambiguation
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Improving data driven wordclass tagging by system combination
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Applying system combination to base noun phrase identification
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 2
Modeling consensus: classifier combination for word sense disambiguation
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Named entity recognition through classifier combination
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
ACLdemo '04 Proceedings of the ACL 2004 on Interactive poster and demonstration sessions
Developing Position Structure-Based Framework for Chinese Entity Relation Extraction
ACM Transactions on Asian Language Information Processing (TALIP)
Named entity recognition with multiple segment representations
Information Processing and Management: an International Journal
Aggregating semantic annotators
Proceedings of the VLDB Endowment
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Several NLP tasks are characterized by asymmetric data where one class label NONE, signifying the absence of any structure (named entity, coreference, relation, etc.) dominates all other classes. Classifiers built on such data typically have a higher precision and a lower recall and tend to overproduce the NONE class. We present a novel scheme for voting among a committee of classifiers that can significantly boost the recall in such situations. We demonstrate results showing up to a 16% relative improvement in ACE value for the 2004 ACE relation extraction task for English, Arabic and Chinese.