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Word-Sequence Kernels

Nicola Cancedda, Eric Gaussier, Cyril Goutte, Jean-Michel Renders
We address the problem of categorising documents using kernel-based methods such as support vector machines. Since the work of Joachims (1998), there is ample experimental evidence that SVM using the standard word frequencies as features yield state-of-the-art performance on a number of benchmark problems. Recently, Lodhi et al. (2002) proposed the use of string kernels, a novel way of computing document similarity based of matching non-consecutive subsequences of characters. In this articles, we propose the use of this technique with sequences of words rather than characters. This approach has several advantages, in particular it is more efficient computationally and it ties in closely with standard linguistic pre-processing techniques. We present additional extension to sequence kernels dealing with symbol-dependent and match-dependent decay factors, soft-matching of symbols, and the implementation of sequence kernels for cross-lingual document similarity.
The Journal of Machine Learning Research


cancedda03a.pdf (235.79 kB)