Then, we design advanced similarity functions between such structures, i.e., semantic tree kernel functions, for exploiting distributional and grammatical information in Support Vector Machines.

The nice property of kernel functions is that they can be used in place of the scalar product of feature vectors to train algorithms such as Support Vector Machines (SVMs).

In line with the method discussed in (Pighin and Moschitti, 2009b), these fragments are extracted as they appear in most of the support vectors selected during SVM training.

the underlying support vectors ) confirm very interesting grammatical generalizations, i.e.

In the first experiment, we compare the prediction accuracy of our SME model to a widely used discriminative learner in NLP — the linear kernel support vector machine (SVM)3.

Table 1: Compare the accuracy of the linear kernel support vector machine to our sparse mixed-effects model in the region and time identification tasks (K = 25).

Traditional discriminative methods, such as support vector machine (SVM) and logistic regression, have been very popular in various text categorization tasks (J oachims, 1998; Wang and McKe-own, 2010) in the past decades.