A Local Alignment Kernel in the Context of NLP

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.

• Type: Conference
• Year: 2008
• Where: COLING
• Authors: Sophia Katrenko, Pieter W. Adriaans
• Keywords: COLING 2008, Computational Linguistics, Distributional Similarity Measures, Local Alignment Kernel, Relation Extraction Task