Original Contribution: Stacked generalization
Neural Networks
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Maximum Entropy Markov Models for Information Extraction and Segmentation
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
SNoW User Guide
Noun phrase recognition by system combination
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Kernel-based discriminative learning algorithms for labeling sequences, trees, and graphs
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Shallow parsing with conditional random fields
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - 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
Named Entity Extraction using AdaBoost
COLING-02 proceedings of the 6th conference on Natural language learning - Volume 20
Introduction to the CoNLL-2003 shared task: language-independent 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
Improved morpho-phonological sequence processing with constraint satisfaction inference
SIGPHON '06 Proceedings of the Eighth Meeting of the ACL Special Interest Group on Computational Phonology and Morphology
Industrially oriented voice control system
Robotics and Computer-Integrated Manufacturing
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Symbolic machine-learning classifiers are known to suffer from near-sightedness when performing sequence segmentation (chunking) tasks in natural language processing: without special architectural additions they are oblivious of the decisions they made earlier when making new ones. We introduce a new pointwise-prediction single-classifier method that predicts trigrams of class labels on the basis of windowed input sequences, and uses a simple voting mechanism to decide on the labels in the final output sequence. We apply the method to maximum-entropy, sparse-winnow, and memory-based classifiers using three different sentence-level chunking tasks, and show that the method is able to boost generalization performance in most experiments, attaining error reductions of up to 51%. We compare and combine the method with two known alternative methods to combat near-sightedness, viz. a feedback-loop method and a stacking method, using the memory-based classifier. The combination with a feedback loop suffers from the label bias problem, while the combination with a stacking method produces the best overall results.