Kernel-based learning to rank with syntactic and semantic structures

Kernel Methods (KMs) are powerful machine learning techniques that can alleviate the data representation problem as they substitute scalar product between feature vectors with similarity functions (kernels) directly defined between data instances, e.g., syntactic trees, (thus features are not needed any longer). This tutorial aims at introducing essential and simplified theory of Support Vector Machines and KMs for the design of practical applications. It will describe effective kernels for easily engineering automatic classifiers and learning to rank algorithms using structured data and semantic processing. Some examples will be drawn from Question Answering, Passage Re-ranking, Short and Long Text Categorization, Relation Extraction, Named Entity Recognition, Co-Reference Resolution. Moreover, some practical demonstrations will be given using the SVM-Light-TK (tree kernel) toolkit.