Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Kernel methods, syntax and semantics for relational text categorization
Proceedings of the 17th ACM conference on Information and knowledge management
Linguistic kernels for answer re-ranking in question answering systems
Information Processing and Management: an International Journal
Efficient convolution kernels for dependency and constituent syntactic trees
ECML'06 Proceedings of the 17th European conference on Machine Learning
Structural relationships for large-scale learning of answer re-ranking
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
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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.