IR Series - Jan Wiebe - Friday, Feb. 22nd - 12:00 pm (noon) - NSH 3002

[Jaime Arguello]

Greetings,

Please join us for our first IR Series talk this spring!

Lunch will be provided by Yahoo!

Speaker: Jan Wiebe
Professor, Department of Computer Science
Director, Intelligent Systems Program
University of Pittsburgh

Date/Time: Friday, 22nd, 12:00 pm (noon)

Location: 3002 Newell-Simon Hall (NSH)

Title: Subjectivity Analysis

Abstract: A growing area of research, "subjectivity analysis", is the 
computational study of affect, opinions, and sentiments expressed in 
text.  Blogs, editorials, reviews (of products, movies, books, etc.), 
and even "objective" newspaper articles (which include many opinions and 
sentiments) are just some of the genres for which accurate 
identification and interpretation of opinions is critical for full text 
understanding.  Subjectivity analysis will support developing tools for 
information analysts in governmental, commercial, and political domains 
who want to automatically track attitudes and feelings in the news and 
on-line forums.  How do people feel about the latest iPod?  Is there a 
change in the support for the new Medicare bill?  A system able to 
automatically identify and extract opinions and sentiments from text 
would be an enormous help to someone sifting through the vast amounts of 
news and web data, trying to answer these kinds of questions.  In this 
talk, I will first give an overview of our work in subjectivity 
analysis, and then will focus on experiments exploring interactions 
between subjectivity and word sense, showing that subjectivity is a 
property that can be associated with word meanings and that subjectivity 
classification can be beneficial for word sense disambiguation.

Bio: My research areas are artificial intelligence and natural language 
processing (NLP). My work with students and colleagues has been in 
discourse processing, pragmatics, word-sense disambiguation, and 
probabilistic classification in NLP. Our most recent work investigates 
automatically recognizing and interpretating expressions of opinions and 
sentiments in text, to support NLP applications such as question 
answering, information extraction, text categorization, and summarization.