The nature of statistical learning theory
The nature of statistical learning theory
Discriminative Reranking for Natural Language Parsing
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
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
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
An SVM based voting algorithm with application to parse reranking
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Discriminative Reranking for Natural Language Parsing
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
An efficient algorithm for learning to rank from preference graphs
Machine Learning
A Sparse Regularized Least-Squares Preference Learning Algorithm
Proceedings of the 2008 conference on Tenth Scandinavian Conference on Artificial Intelligence: SCAI 2008
Locality kernels for sequential data and their applications to parse ranking
Applied Intelligence
A probabilistic search for the best solution among partially completed candidates
CHSLP '06 Proceedings of the Workshop on Computationally Hard Problems and Joint Inference in Speech and Language Processing
Relevance ranking of intensive care nursing narratives
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part I
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
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We present an adaptation of the Regularized Least-Squares algorithm for the rank learning problem and an application of the method to reranking of the parses produced by the Link Grammar (LG) dependency parser. We study the use of several grammatically motivated features extracted from parses and evaluate the ranker with individual features and the combination of all features on a set of biomedical sentences annotated for syntactic dependencies. Using a parse goodness function based on the F-score, we demonstrate that our method produces a statistically significant increase in rank correlation from 0.18 to 0.42 compared to the built-in ranking heuristics of the LG parser. Further, we analyze the performance of our ranker with respect to the number of sentences and parses per sentence used for training and illustrate that the method is applicable to sparse datasets, showing improved performance with as few as 100 training sentences.