A compositional perspective in convolution kernels

Kernel-based learning has been largely adopted in many semantic textual inference tasks. In particular, Tree Kernels (TKs) have been successfully applied 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-scale corpora. Recently, Compositional Semantics phenomena arising in complex linguistic structures have been studied in an extended paradigm called Distributional Compositional Semantics (DCS), where, for example, algebraic operators on lexical vectors have been defined to account for grammatically typed bi-grams or complex verb or noun phrases. In this paper, a novel kernel called Compositionally Smoothed Partial Tree Kernel is presented to integrate DCS operators into the tree kernel evaluation by also considering complex compositional nodes. Empirical results on well-known NLP 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 this kernel.