Tree kernel

In machine learning, tree kernels are the application of the more general concept of positive-definite kernel to tree structures. They find applications in natural language processing, where they can be used for machine-learned parsing or classification of sentences. In machine learning, tree kernels are the application of the more general concept of positive-definite kernel to tree structures. They find applications in natural language processing, where they can be used for machine-learned parsing or classification of sentences. In natural language processing, it is often necessary to compare tree structures (e.g. parse trees) for similarity. Such comparisons can be performed by computing dot products of vectors of features of the trees, but these vectors tend to be very large: NLP techniques have come to a point where a simple dependency relation over two words is encoded with a vector of several millions of features. It can be impractical to represent complex structures such as trees with features vectors. Well-designed kernels allow computing similarity over trees without explicitly computing the feature vectors of these trees. Moreover, kernel methods have been widely used in machine learning tasks ( e.g. SVM ), and thus plenty of algorithms are working natively with kernels, or have an extension that handles kernelization. An example application is classification of sentences, such as different types of questions.