Kernel-based methods for NLP tasks have been shown to enable robust and effective learning, although their inherent complexity is manifest also in Online Learning (OL) scenarios, where time and memory usage grows along with the arrival of new examples. A state-of-the-art budgeted OL algorithm is here extended to efficiently integrate complex kernels by constraining the overall complexity. Principles of Fairness and Weight Adjustment are applied to mitigate imbalance in data and improve the model stability. Results in Sentiment Analysis in Twitter and Question Classification show that performances very close to the state-of-the-art achieved by batch algorithms can be obtained. © 2014 Springer International Publishing Switzerland.

Filice, S., Castellucci, G., Croce, D., Basili, R. (2014). Effective kernelized online learning in language processing tasks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp.347-358). Springer Verlag [10.1007/978-3-319-06028-6_29].

Effective kernelized online learning in language processing tasks

CROCE, DANILO;BASILI, ROBERTO
2014-04-01

Abstract

Kernel-based methods for NLP tasks have been shown to enable robust and effective learning, although their inherent complexity is manifest also in Online Learning (OL) scenarios, where time and memory usage grows along with the arrival of new examples. A state-of-the-art budgeted OL algorithm is here extended to efficiently integrate complex kernels by constraining the overall complexity. Principles of Fairness and Weight Adjustment are applied to mitigate imbalance in data and improve the model stability. Results in Sentiment Analysis in Twitter and Question Classification show that performances very close to the state-of-the-art achieved by batch algorithms can be obtained. © 2014 Springer International Publishing Switzerland.
36th European Conference on Information Retrieval, ECIR 2014
Amsterdam, nld
2014
City of Amsterdam
Rilevanza internazionale
apr-2014
Settore ING-INF/05 - SISTEMI DI ELABORAZIONE DELLE INFORMAZIONI
Settore INF/01 - INFORMATICA
English
Computer Science (all); Theoretical Computer Science
Intervento a convegno
Filice, S., Castellucci, G., Croce, D., Basili, R. (2014). Effective kernelized online learning in language processing tasks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp.347-358). Springer Verlag [10.1007/978-3-319-06028-6_29].
Filice, S; Castellucci, G; Croce, D; Basili, R
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2108/124192
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