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Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Everything old is new again: a fresh look at historical approaches in machine learning
Everything old is new again: a fresh look at historical approaches in machine learning
Text classification using string kernels
The Journal of Machine Learning Research
The Journal of Machine Learning Research
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Convolution kernels with feature selection for natural language processing tasks
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Analysis of link grammar on biomedical dependency corpus targeted at protein-protein interactions
JNLPBA '04 Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications
Incorporating external information in bayesian classifiers via linear feature transformations
FinTAL'06 Proceedings of the 5th international conference on Advances in Natural Language Processing
Locality-convolution kernel and its application to dependency parse ranking
IEA/AIE'06 Proceedings of the 19th international conference on Advances in Applied Artificial Intelligence: industrial, Engineering and Other Applications of Applied Intelligent Systems
Regularized least-squares for parse ranking
IDA'05 Proceedings of the 6th international conference on Advances in Intelligent Data Analysis
Accelerated max-margin multiple kernel learning
Applied Intelligence
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We propose a framework for constructing kernels that take advantage of local correlations in sequential data. The kernels designed using the proposed framework measure parse similarities locally, within a small window constructed around each matching feature. Furthermore, we propose to incorporate positional information inside the window and consider different ways to do this. We applied the kernels together with regularized least-squares (RLS) algorithm to the task of dependency parse ranking using the dataset containing parses obtained from a manually annotated biomedical corpus of 1100 sentences. Our experiments show that RLS with kernels incorporating positional information perform better than RLS with the baseline kernel functions. This performance gain is statistically significant.