Monday, February 18, 2008

Jan Wiebe -- Subjectivity Analysis -- Friday, Feburary 22nd 2008, 12:00 pm (noon)

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.