Title: Neural Networks and Supervised Embedding Models for NLP and Retrieval Speaker: Jason Weston (Google) Place: Science Center. Rm 4102, CUNY Graduate Center. 5th Ave & 34th St. Abstract: I will give a summary of my work of applying both simple ("supervised embedding") and a bit more complex ("deep learning") neural networks to the fields of NLP and text retrieval: - Multi-tasking multilayer neural networks for the tasks of part-of-speech tagging, chunking, named entity recognition and semantic role labeling. - Document retrieval using supervised embedding models (including dealing with scalability, diversity and ambiguity). - Utilizing world knowledge (in the form of knowledge bases) to improve concept tagging and word sense disambiguation. Bio: Jason Weston is a Research Scientist at Google NY since July 2009. He earned his PhD in machine learning at Royal Holloway, University of London and at AT&T Research in Red Bank, NJ (advisor: Vladimir Vapnik) in 2000. From 2000 to 2002, he was a Researcher at Biowulf technologies, New York. From 2002 to 2003 he was a Research Scientist at the Max Planck Institute for Biological Cybernetics, Tuebingen, Germany. From 2003 to June 2009 he was a Research Staff Member at NEC Labs America, Princeton. His interests lie in statistical machine learning and its application to text, audio and images. Jason has published over 80 papers, including best paper awards at ICML and ECML.