Time: 2pm-3pm, Friday, Nov. 12 
Place: Room 4102, CUNY Graduate Center, 365 Fifth Ave (34str&35str). 
Speaker: Dr. Dan Bikel (Google) 
Title: Language Technology Research at Google 

Abstract: 
Google's mission is to organize the world's information and make it 
accessible. While this mission statement is somewhat vague, it 
certainly includes understanding the information on the web and being 
able to present that information in coherent and succinct ways to 
users. In this talk, I will give an overview of the various kinds of 
language processing technology research that is going on at Google, 
including (but not limited to): machine translation, speech 
recognition, large-scale language modeling and sentiment analysis. I 
will also discuss my current primary research project, which is the 
improvement of speech recognition on YouTube videos. 

Bio: 
Dan Bikel graduated with honors from Harvard in 1993 with a degree 
in Classics, after which he spent a year as a graduate student at 
Harvard studying engineering, computer science and music. In 1994, 
he joined the Speech and Language Processing group at BBN in 
Cambridge,Massachusetts, where he co-created the first 
state-of-the-art namedentity finder, Nymble (now known as 
IdentiFinder(tm)). After spending three years at BBN, he became a 
Ph.D. student at the University of Pennsylvania's Computer and 
Information Science department, studying under Prof. Mitchell P. 
Marcus. At Penn, he built the first extensible syntactic parsing 
engine, discovering new and surprising properties of syntactic 
parsing models. Dan spent five years as a Research Staff Member at 
the IBM T. J. Watson Research Center, where he investigated many 
aspects of information extraction, including named entity 
coreference, semantic role labeling, quotation attribution detection, 
event and topic matching, oblivious computation for NLP applications 
and NLP system architectures. Dan is currently a Research Scientist 
at Google Research NYC, exploring ways to improve the quality and 
robustness of speech recognition, statistical parsing and question 
answering systems.