Deterministic part-of-speech tagging with finite-state transducers
Computational Linguistics
Machine Learning
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Ensemble Methods in Machine Learning
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Transformation based learning and data-driven lexical disambiguation: syntactic and semantic ambiguity resolution
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-2002 shared task: language-independent named entity recognition
COLING-02 proceedings of the 6th conference on Natural language learning - Volume 20
Portuguese Part-of-Speech Tagging Using Entropy Guided Transformation Learning
PROPOR '08 Proceedings of the 8th international conference on Computational Processing of the Portuguese Language
Boosting random subspace method
Neural Networks
Combination strategies for semantic role labeling
Journal of Artificial Intelligence Research
Introduction to the CoNLL-2005 shared task: semantic role labeling
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
Combining bagging and random subspaces to create better ensembles
IDA'07 Proceedings of the 7th international conference on Intelligent data analysis
A Token Classification Approach to Dependency Parsing
STIL '09 Proceedings of the 2009 Seventh Brazilian Symposium in Information and Human Language Technology
Clause Identification Using Entropy Guided Transformation Learning
STIL '09 Proceedings of the 2009 Seventh Brazilian Symposium in Information and Human Language Technology
A general and multi-lingual phrase chunking model based on masking method
CICLing'06 Proceedings of the 7th international conference on Computational Linguistics and Intelligent Text Processing
Extracting person names from diverse and noisy OCR text
AND '10 Proceedings of the fourth workshop on Analytics for noisy unstructured text data
Rule and tree ensembles for unrestricted coreference resolution
CONLL Shared Task '11 Proceedings of the Fifteenth Conference on Computational Natural Language Learning: Shared Task
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We present a new ensemble method that uses Entropy Guided Transformation Learning (ETL) as the base learner. The proposed approach, ETL Committee, combines the main ideas of Bagging and Random Subspaces. We also propose a strategy to include redundancy in transformation-based models. To evaluate the effectiveness of the ensemble method, we apply it to three Natural Language Processing tasks: Text Chunking, Named Entity Recognition and Semantic Role Labeling. Our experimental findings indicate that ETL Committee significantly outperforms single ETL models, achieving state-of-the-art competitive results. Some positive characteristics of the proposed ensemble strategy are worth to mention. First, it improves the ETL effectiveness without any additional human effort. Second, it is particularly useful when dealing with very complex tasks that use large feature sets. And finally, the resulting training and classification processes are very easy to parallelize.