Original Contribution: Stacked generalization
Neural Networks
IEEE Transactions on Pattern Analysis and Machine Intelligence
EuroWordNet: a multilingual database with lexical semantic networks
EuroWordNet: a multilingual database with lexical semantic networks
Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
Boosting Applied toe Word Sense Disambiguation
ECML '00 Proceedings of the 11th European Conference on Machine Learning
A methodology for automatic term recognition
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 2
Boosting trees for clause splitting
ConLL '01 Proceedings of the 2001 workshop on Computational Natural Language Learning - Volume 7
The REG summarization system with question reformulation at QA@INEX track 2010
INEX'10 Proceedings of the 9th international conference on Initiative for the evaluation of XML retrieval: comparative evaluation of focused retrieval
Journal of Information Science
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Term extraction is the task of automatically detecting, from textual corpora, lexical units that designate concepts in thematically restricted domains (e.g. medicine). Current systems for term extraction integrate linguistic and statistical cues to perform the detection of terms. The best results have been obtained when some kind of combination of simple base term extractors is performed [14]. In this paper it is shown that this combination can be further improved by posing an additional learning problem of how to find the best combination of base term extractors. Empirical results, using AdaBoost in the metalearning step, show that the ensemble constructed surpasses the performance of all individual extractors and simple voting schemes, obtaining significantly better accuracy figures at all levels of recall.