Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
A Generalized Representer Theorem
COLT '01/EuroCOLT '01 Proceedings of the 14th Annual Conference on Computational Learning Theory and and 5th European Conference on Computational Learning Theory
Text classification using string kernels
The Journal of Machine Learning Research
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Hierarchical directed acyclic graph kernel: methods for structured natural language data
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
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
Regularized least-squares for parse ranking
IDA'05 Proceedings of the 6th international conference on Advances in Intelligent Data Analysis
Locality kernels for sequential data and their applications to parse ranking
Applied Intelligence
Locality kernels for protein classification
WABI'07 Proceedings of the 7th international conference on Algorithms in Bioinformatics
| Hi-index | 0.00 |
We propose a Locality-Convolution (LC) kernel in application to dependency parse ranking. The LC kernel measures parse similarities locally, within a small window constructed around each matching feature. Inside the window it makes use of a position sensitive function to take into account the order of the feature appearance. The similarity between two windows is calculated by computing the product of their common attributes and the kernel value is the sum of the window similarities. We applied the introduced kernel together with Regularized Least-Squares (RLS) algorithm to a dataset containing dependency parses obtained from a manually annotated biomedical corpus of 1100 sentences. Our experiments show that RLS with LC kernel performs better than the baseline method. The results outline the importance of local correlations and the order of feature appearance within the parse. Final validation demonstrates statistically significant increase in parse ranking performance.