In fact, lexical information is highly affected by data-sparseness, thus tree kernels combined with semantic information created from additional resources should provide a way to obtain a more robust system.

The idea is to (i) learn semantic similarity between words on the pivot corpus and (ii) use tree kernels embedding such a similarity to learn a RE system on the source, which allows to generalize to the new target domain.

We encode word clusters or similarity in tree kernels , which, in turn, produce spaces of tree fragments.

Rather than only matching the surface string of words, lexical similarity enables soft matches between similar words in convolution tree kernels .

In the empirical evaluation on Automatic Content Extraction (ACE) data, we evaluate the impact of convolution tree kernels embedding lexical semantic similarities.

Semantic syntactic tree kernels have been previously used for question classification (Bloehdorn and Moschitti, 2007a; Bloehdorn and Moschitti, 2007b; Croce et al., 2011).

Thus, we present a novel application of semantic syntactic tree kernels and Brown clusters for domain adaptation of tree-kernel based relation extraction.

Commonly used kernels in NLP are string kernels (Lodhi et al., 2002) and tree kernels (Moschitti, 2006; Moschitti, 2008).

Figure l: Syntactic tree kernel (STK).

Syntactic tree kernels (Collins and Duffy, 2001) compute the similarity between two trees T1 and T2 by counting common sub-trees (cf.

We introduce a novel tree representation, and use it to train predictive models with tree kernels using support vector machines.

Our semantic frame-based model benefits from tree kernel learning using support vector machines.

To analyze which were the best performing features within sectors, we extracted the best performing frame fragments for the polarity task using a tree kernel feature engineering method presented in Pighin and Moschitti (2009).

3'SVM-light: http://svmlight.joachims.org and Tree Kernels in SVM-light: http://disi.unitn.it/moschitti/Tree-Kernel.htm.

The second is a tree representation that encodes semantic frame features, and depends on tree kernel measures for support vector machine classification.

The tree kernel (Moschitti, 2006; Collins and Duffy, 2002) is a function of tree similarity, based on common substructures (tree fragments).