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

[Grace Hui Yang]

  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.
>