Beck, D., Cohn, T., Hardmeier, C. et al. (1 more author) (2015) Learning Structural Kernels for Natural Language Processing. In: Transactions of the Association for Computational Linguistics. Conference on Empirical Methods in Natural Language Processing, September 17–21, 2015, Lisbon, Portugal. Association for Computational Linguistics , pp. 461-473.
Abstract
Structural kernels are a flexible learning paradigm that has been widely used in Natural Language Processing. However, the problem of model selection in kernel-based methods is usually overlooked. Previous approaches mostly rely on setting default values for kernel hyperparameters or using grid search, which is slow and coarse-grained. In contrast, Bayesian methods allow efficient model selection by maximizing the evidence on the training data through gradient-based methods. In this paper we show how to perform this in the context of structural kernels by using Gaussian Processes. Experimental results on tree kernels show that this procedure results in better prediction performance compared to hyperparameter optimization via grid search. The framework proposed in this paper can be adapted to other structures besides trees, e.g., strings and graphs, thereby extending the utility of kernel-based methods.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2015 Association for Computational Linguistics. This work is licensed under a Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/). |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 26 Apr 2016 14:44 |
Last Modified: | 26 Apr 2016 14:44 |
Published Version: | https://tacl2013.cs.columbia.edu/ojs/index.php/tac... |
Status: | Published |
Publisher: | Association for Computational Linguistics |
Refereed: | Yes |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:97934 |