Paper: A Local Alignment Kernel in the Context of NLP

ACL ID C08-1053
Title A Local Alignment Kernel in the Context of NLP
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2008
Authors

This paper discusses local alignment ker- nels in the context of the relation extrac- tion task. We de ne a local alignment kernel based on the Smith-Waterman mea- sure as a sequence similarity metric and proceed with a range of possibilities for computing a similarity between elements of sequences. We propose to use distri- butional 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 sug- gest that a LA kernel provides promising results on some biomedical corpora largely outperforming a baseline.