Upcoming IR Talk at CMU: John Tait

The upcoming IR-related LTI Seminar talk, John Tait on Patent Retrieval.

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.

New Home

Welcome to the new home of the CMU Information Retrieval Discussion Series. We're in the process of moving the old site here, so please be patient.

Past IR-Series Presentations

Friday, November 2, 2007 - 12:00-1:00 pm, Newell-Simon Hall (NSH) 3002
Title: CMU at TREC 2007
Speakers: Jonathan Elsas, Le Zhao and Yangbo Zhu (CMU)

Friday, October 5, 2007 - 12:00-1:00 pm, Newell-Simon Hall (NSH) 3002
Title: Estimating and Exploiting Uncertainty in Pseudo-Relevance Feedback
Speakers: Kevyn Collins-Thompson (CMU)

Friday, July 13, 2007 - 12:00-1:00 pm, Newell-Simon Hall (NSH) 3002
Title: Utility-based Information Distillation Over Temporally Sequenced Documents
Speakers: Yiming Yang (CMU)

Friday, May 18, 2007 - 12:00-1:00 pm, Newell-Simon Hall (NSH) 3002
Title: Collaborative Web Search - Exploiting User Activity for User Benefit
Speaker: Jill Freyne (University College Dublin)
Details

Friday, January 19, 2007 - 12:00 NSH 3002
Title: Using Graphs and Random Walks to Discover Latent Similarities in Text
Speaker: Gunes Erkan
Details

Friday, November 10, 2006, 2007 - 12:00 NSH 3002
Title: Personal Metasearch
Speaker: Paul Thomas
Details

Friday, May 19, 2006 - 12:00 NSH 3002
Title: Collaborative Adaptive User Profile with Implicit and Explicit User Feedback
Speaker: Yi Zhang
Details

Wednesday, April 19, 2006 - 12:00, NSH 3002
Title: Deriving Marketing Intelligence from Online Discussion
Speaker: Matthew Hurst and Natalie Glance
Details

Wednesday, April 5, 2006 - 12:00, NSH 3002
Title: A Graphical Framework for Contextual Search and Name Disambiguation in Email
Speaker: Einat Minkov
Details

Wednesday, March 8, 2006 - 12:00, NSH 3002
Title: Structured and Dynamic Topic Models
Speaker: John Lafferty
Details

Wednesday, February 22, 2006 - 12:00, NSH 3002
Title: Automatically Labeling Hierarchical Clusters
Speaker: Pucktada (Puck) Treeratpituk
Details

Friday, June 3, 2005 - 3:30, WeH 5409
Title: PageRank without Hyperlinks: Structural Re-ranking using Links Induced by Language Models
Speaker: Oren Kurland
Details

Wednesday, April 27, 2005 - 4:30, WeH 4601
Title: Dynamic Construction of Content-Based Topologies in Hierarchical Peer-to-Peer Networks
Speaker: Jie Lu
Details

Wednesday, March 16, 2005 - 4:30, WeH 4601
Title: Modeling Search Engine Effectiveness for Federated Search
Speaker: Luo Si
Details

Wednesday, March 2, 2005 - 4:30, WeH 4623
Title: What is the matter? Explorations in text categorization
Speaker: Lillian Lee
Details

Wednesday, January 19th, 2005 - 4:30, WeH 4601
Title: Detecting Action-Items in E-mail
Speaker: Paul N. Bennett
Details

Wednesday, December 1, 2004 - 3:00, WeH 4625
Title: Probabilistic Models of Text and Images
Speaker: David Blei
Details

Wednesday, November 17, 2004 - 3:00, WeH 4625
Title: Merging Rank Lists from Multiple Sources in Video Classification
Speaker: Wei-Hao Lin
Details

Wednesday, November 10, 2004 - 3:00, WeH 4625
Title: Associating Names with Persons in Broadcast News Video
Speaker: Jun Yang
Details

Wednesday, October 20, 2004 - 3:00, WeH 4625
Title: Graph Mining
Speaker: Christos Faloutsos
Details

Wednesday, October 6, 2004 - 3:00, WeH 4625
Topic: Review of the SIGIR 2004 Best Paper, “ A Formal Study of Information Retrieval Heuristics” by Hui Fang, Tao Tao, and ChengXiang Zhai
Speaker: Kevyn Collins-Thompson
Details

Friday, October 1, 2004 - 1:30, NSH 4513
Title: Combining Language Modeling Approach with String-matching in Near-Duplicate Detection in E-Rulemaking
Speaker: Puck Treeratpituk
Details

Wednesday, September 22, 2004 - 2:30, NSH 4632
Learning to Summarize Interviews for Project Reports
Nikesh Garera
Details

Thursday, August 26, 2004 - 3:30, WeH 4625
Analyzing Time Series Gene Expression Data
Jason Ernst
Details

Tuesday, August 17, 2004 - 2:00, WeH 4625
Learning Table Extraction from Examples
Ashwin Tengli
Details

Thursday, August 12, 2004 - 3:30, WeH 4625
Learning to Classify Email into "Speech Acts"
Vitor Carvalho
Details

Thursday, July 8, 2004 - 3:30, WeH 4625
Resource Selection for Domain-Specific Cross-Lingual IR
Monica Rogati
Details

Tuesday, January 22, 2004 - 12:00, NSH 4513
Dynamic Recommender System on User Taste Tendency Model
Soojung Lee
Details

Thursday, December 4, 2003 - 12:00, NSH 4513
The Robustness of Content-Based Search in Hierarchical Peer to Peer Networks
M. Elena Renda
Details

Thursday, October 30, 2003 - 12:00, NSH 4632
Boosting Support Vector Machines for Text Classification through Parameter-free Threshold Relaxation
Dr. James G. Shanahan
Details

Thursday, October 23, 2003 - 12:00, NSH 4632
Content-Based Retrieval in Hybrid Peer-to-Peer Networks
Jie Lu
Details

Thursday, October 16, 2003 - 12:00, NSH 4632
The Utility of Question Analysis in an Open-Domain Question Answering System
Yifen Huang
Details

Thursday, August 28, 2003 - 3:30, NSH 4632
Searching Peer-to-Peer Networks
Dr. Bin Yu
Details

Thursday, August 14, 2003 - 3:30, NSH 3001
Flexible Mixture Model for Collaborative Filtering
Luo Si

Modified Logistic Regression: An Approximation to SVM and its Applications in Large-Scale Text Categorization
Jian Zhang
Details

Thursday, June 19, 2003 - 3:30, NSH 3001
Improving Text Classifier Probability Estimates
Paul Bennett
Details

Thursday, June 5, 2003 - 3:30, NSH 3002
Radio Station Playlist Generation
Andrew P. Widdowson
Details

Thursday, May 22, 2003 - 3:30, NSH 3001
Discussion on Secondary Structure Prediction for Protein Sequences
Yan Liu
Details

Thursday, May 8, 2003 - 3:30, NSH 3001
Negative Pseudo Relevance Feedback for Multimedia Retrieval
Rong Yan
Details

Thursday, April 10, 2003 - 3:30, NSH 3001
Web Image Retrieval Re-Ranking with Relevance Model
Wei-Hao Lin
Details

Thursday, March 27, 2003 - 3:30, NSH 3001
Clustering Genes
Fan Li
Details

Thursday, March 13, 2003 - 3:30, NSH 3001
Exploration and Exploitation in Adaptive Filtering Based on Bayesian Active Learning
Yi Zhang
Details

Thursday, February 27, 2003 - 3:30, NSH 3001
Beyond Independent Topical Relevance: Evaluation Metrics and Methods for Aspect Retrieval
Dr. William Cohen
Details

Thursday, February 13, 2003 - 3:30, NSH 3001
Overview of Database Selection Methods
Luo Si
Details

Tuesday, January 14, 2003 - 11:00-12:30, Wean 4632
Topics and Techniques in (Structured) Document Retrieval
Paul Ogilvie
Details

Instructions for Presenters

As the discussion series has progressed, our goals for the presentations have evolved. In addition to generating discussion among researchers, we have come to recognize the value of the series as a means to refine our presentation skills. In light of this, here are some guidelines for creating your presentation:

• When preparing your presentation, view this as a normal conference talk and prepare accordingly.
• Please prepare a short (20-30 minute) talk or a long (45 minute) talk according to the time slot the organizer has reserved.
• You should assume that the audience is knowledgeable in IR and many of the techniques commonly used in the field. Unless the purpose of your talk is a general overview of a research problem, you should assume that the related research can be covered very briefly (one or two slides).
• Focus on presenting your thoughts, issues, and contributions to the problem at hand.
• If you have extra material that won't fit in the talk, prepare slides for them as it is very likely that we will be willing to hear more about the subject after the main talk is over.
• Please don't be afraid to present work in progress. Even with the change of presentation format to conference talk style , we are still driven by our original goals of learning about current research and fostering collaboration on work in progress.

Thanks are due to Yiming Yang for some helpful suggestions.

About the Series

The CMU-IR Discussion Series is a student-run initiative to encourage discussion and foster research between the multiple Information Retrieval research groups here at Carnegie Mellon. We intend the informal presentations to be a vehicle for:

• learning about each others' research,
• discussing the big (and little) problems of a research area, and
• fostering collaboration across groups.

The presentations in this series are of an informal nature. Interruptions, questions, and discussions on tangents are encouraged and welcomed. We wish this to be a low-stress, relaxed opportunity for our presenters to discuss topics of interest with us. Our interpretation of the meaning of "information retrieval" is fairly broad; we welcome discussions of related fields (Machine Learning, Agents, Statistics, and so on). While most of the presentations are from students researching Information Retrieval here at CMU, we also welcome discussions from students and faculty in other programs and institutions as well as industry professionals.

One of the goals of the series is to strike a nice balance between area overview presentations and technical presentations on specific approaches. Because many of us work on quite different areas of Information Retrieval, we often find it beneficial to have discussions that focus on the important problems in our respective research areas and the techniques that have been found to be broadly useful (and occasionally the spectacular failures). In order to keep grounded, we also have some technical discussions on specific techniques and approaches.

In an effort to make this series a valuable resource for others, we plan on posting the authors' slides (with permission). We also ask that authors provide a short reading list of articles (preferably online) for people who want to learn about the topics in more depth.

For LTI students: a presentation in the IR Discussion Series can fulfill your annual LTI talk requirement. Let us know if you wish to do this more than a week in advance, so that we can advertise the talk according to policy. You will still be required to make sure two faculty are present and that they fill out the form after your presentation.