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Effective Kernelized Online Learning in Language Processing Tasks

  • Simone Filice
  • Giuseppe Castellucci
  • Danilo Croce
  • Roberto Basili
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8416)

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.

Keywords

Support Vector Online Learn Sentiment Analysis Kernel Computation Weight Adjustment 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Simone Filice
    • 1
  • Giuseppe Castellucci
    • 2
  • Danilo Croce
    • 3
  • Roberto Basili
    • 3
  1. 1.DICIIUniversity of RomaRomaItaly
  2. 2.DIEUniversity of RomaRomaItaly
  3. 3.DIIUniversity of RomaRomaItaly

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