Syllables and other String Kernel Extensions
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Text classification using string kernels
The Journal of Machine Learning Research
Kernel methods for relation extraction
The Journal of Machine Learning Research
An empirical study of smoothing techniques for language modeling
ACL '96 Proceedings of the 34th annual meeting on Association for Computational Linguistics
Measures of distributional similarity
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Protein homology detection using string alignment kernels
Bioinformatics
Dependency tree kernels for relation extraction
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Unsupervised construction of large paraphrase corpora: exploiting massively parallel news sources
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Characterising measures of lexical distributional similarity
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
A shortest path dependency kernel for relation extraction
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
RelEx---Relation extraction using dependency parse trees
Bioinformatics
Finding synonyms using automatic word alignment and measures of distributional similarity
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
Applying spelling error correction techniques for improving semantic role labelling
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
Efficient convolution kernels for dependency and constituent syntactic trees
ECML'06 Proceedings of the 17th European conference on Machine Learning
Extracting complex biological events with rich graph-based feature sets
BioNLP '09 Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing: Shared Task
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This paper discusses local alignment kernels in the context of the relation extraction task. We define a local alignment kernel based on the Smith-Waterman measure as a sequence similarity metric and proceed with a range of possibilities for computing a similarity between elements of sequences. We propose to use distributional similarity measures on elements and by doing so we are able to incorporate extra information from the unlabeled data into a learning task. Our experiments suggest that a LA kernel provides promising results on some biomedical corpora largely outperforming a baseline.