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- W38723169 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." @default.
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- W38723169 date "2014-01-01" @default.
- W38723169 modified "2023-09-25" @default.
- W38723169 title "Effective Kernelized Online Learning in Language Processing Tasks" @default.
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- W38723169 doi "https://doi.org/10.1007/978-3-319-06028-6_29" @default.
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