Learning in Web Search ( LWS 2005 )
Support Vector Machines (SVMs) have been one of the major breakthroughs in machine learning, both in terms of their practical success as well as their learning-theoretic properties. This talk presents a generic extension of SVM classification to the case of structured classification, i.e. the task of predicting output variables with some meaningful internal structure. As we will show, this approach has many interesting applications in information extraction, information retrieval, document categorization and natural language processing, including supervised training of Markov Random Fields and probabilistic context-free grammars.