ETL ensembles for chunking, NER and SRL
Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
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Computational Linguistics and Intelligent Text Processing: 11th International Conference, CICLing 2010, Iaşi, Romania, March 21-27, 2010. Proceedings. ed. / Alexander Gelbukh. Berlin: Springer Verlag, 2010. p. 100-112 (Lecture Notes in Computer Science; Vol. 6008 ).
Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
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TY - CHAP
T1 - ETL ensembles for chunking, NER and SRL
AU - dos Santos, Cícero N.
AU - Milidiú, Ruy L.
AU - Crestana, Carlos E.M.
AU - Fernandes, Eraldo R.
N1 - Conference code: 11
PY - 2010
Y1 - 2010
N2 - 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 areworth 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.
AB - 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 areworth 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.
KW - Ensemble methods
KW - Entropy guided transformation learning
KW - Named entity recognition
KW - Semantic role labeling
KW - Text chunking
KW - Informatics
KW - Business informatics
UR - http://www.scopus.com/inward/record.url?scp=78650569670&partnerID=8YFLogxK
UR - https://d-nb.info/1000252183
U2 - 10.1007/978-3-642-12116-6_9
DO - 10.1007/978-3-642-12116-6_9
M3 - Article in conference proceedings
AN - SCOPUS:78650569670
SN - 3-642-12115-2
SN - 978-3-642-12115-9
T3 - Lecture Notes in Computer Science
SP - 100
EP - 112
BT - Computational Linguistics and Intelligent Text Processing
A2 - Gelbukh, Alexander
PB - Springer Verlag
CY - Berlin
T2 - 11th International Conference on Computational Linguistics and Intelligent Text Processing - CICLing 2010
Y2 - 21 March 2010 through 27 March 2010
ER -