Kernel-based learning has been largely applied to semantic textual inference tasks. In particular, Tree Kernels (TKs) are crucial in the modeling of syntactic similarity between linguistic instances in Question Answering or Information Extraction tasks. At the same time, lexical semantic information has been studied through the adoption of the so-called Distributional Semantics (DS) paradigm, where lexical vectors are acquired automatically from large corpora. Notice how methods to account for compositional linguistic structures (e.g. grammatically typed bi-grams or complex verb or noun phrases) have been proposed recently by defining algebras on lexical vectors. The result is an extended paradigm called Distributional Compositional Semantics (DCS). Although lexical extensions have been already proposed to generalize TKs towards semantic phenomena (e.g. the predicate argument structures as for role labeling), currently studied TKs do not account for compositionality, in general. In this paper, a novel kernel called Compositionally Smoothed Partial Tree Kernel is proposed to integrate DCS operators into the tree kernel evaluation, by acting both over lexical leaves and non-terminal, i.e. complex compositional, nodes. The empirical results obtained on a Question Classification and Paraphrase Identification tasks show that state-of-the-art performances can be achieved, without resorting to manual feature engineering, thus suggesting that a large set of Web and text mining tasks can be handled successfully by the kernel proposed here.
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