Title: Interpreting the Temporal Aspects of Language
Speaker: Naushad UzZaman (Rochester)
Time: 2:15pm-3:30pm, Friday, Feb 24, 2012
Place: Room 4102, CUNY Graduate Center, 365 Fifth Ave (34str&35str).
		
Understanding temporal information in the text is fundamental for deep
language understanding and key to many advanced NLP applications, such as
question answering, information extraction, timeline visualization, and
document summarization. These techniques can be applied in news, medical,
history and other domains.

In this talk, I will present our hybrid system to automatically extract
temporal information from raw text by extracting events, temporal
expressions and identifying temporal relations between them. Our system had
a competitive performance in the temporal evaluation shared task - TempEval
2010. Next, I will present a metric that we developed for evaluation of
temporal annotation. Our metric has been adopted by the premier temporal
evaluation shared task, TempEval 2013, for evaluating participating systems.
Finally, I will present a question-answering system that can answer temporal
questions with temporal reasoning. Our developed QA system can also be used
to evaluate temporal information understanding capability.

I will also briefly talk about my other projects, ranging from multimodal
summarization of complex sentence to game prediction using social media.

Bio: 

Naushad UzZaman is a PhD candidate under Professor James F. Allen in the 
Computer Science department at the University of Rochester (URCS). His
research interests are in Natural Language Understanding (NLU) and Natural
Language Processing (NLP), with focus on Temporal Information Processing,
Information Extraction, Social Media Text Analysis, Medical NLP, Question
Answering, and Multimodal Summarization. At URCS, he primarily worked on
temporal information processing. He is co-organizing the TempEval 2013
shared task. He also worked on making information accessible with Jeffrey
Bigham. He has done multiple research internships in various domains, such
as, game prediction using social media (Yahoo! Research), medical NLP
(Microsoft Medical Media Lab), and car dialog systems (Bosch Research and
Technology Center).