William W. Cohen Associate Research Professor, Machine Learning Department, Carnegie Mellon University Member of the Language Technology Institute; the joint CMU-Pitt Program in Computational Biology; the Lane Center for Computational Biology, and the Center for Bioimage Informatics.

Announcements

Biography

Dr. Cohen is also General Chair for the 2008 International Machine Learning Conference, which will be held July 6-9 at the University of Helsinki, in Finland.

Dr. Cohen's research interests include information integration and machine learning, particularly information extraction, text categorization and learning from large datasets. He holds seven patents related to learning, discovery, information retrieval, and data integration, and is the author of more than 100 publications.

Projects

• SLIF, a system that analyzes the text and images in online journal articles to find information about the subcellular localization of proteins.
• CALO and RADAR, two large projects focused on intelligent agents for office automation.
• The Pittsburgh Science of Learning Center, notably in a project to add learning-by-demonstration to CTAT.

Software and demos

• Measure twice, cut once - Vitor and Ramnath have developed a Thunderbird plugin that implements recipient recommendation and leak detection for email. It modifies Thunderbird by adding an additional pane that pops up after you send a message, giving you one final chance to fix any errors in your recipient list. There's a brief writeup on how to use it, but it's pretty self-explanatory: just download it, open Thunderbird, and go to the tools->addon menu to install. After you've installed it, you train by opening your folder of "Sent" mail and pressing the "train" button. (This took about an hour for my 9000+ old messages.)
• Ramesh Nallapati has put together two nice demos of his multiscale topic tomography topic-modeling technique, one for articles from Science, and one with cancer-related articles from PubMed.
• And here's a cool demo of a Google-Sets-like bootstrapping tool written by my student Richard Wang.
• Here are two movies that demo SimStudent, a programming-by-demonstration system for constructing cognitive tutors, built by Noboru Matsuda.
  • Interactive mode (solves problems proactively, as way of posing queries)
  • Non-interactive mode

• Minorthird is an open-source Java package of information extraction and text classification learning tools.
  • I am now distributing a standalone tool, built on Minorthird, for annotating biomedical text. This is particularly aimed at annotating figure captions but might be useful for other text as well. The jar file for this is rather large (17M), as it includes a Minorthird jar. There is documentation available for this, and some sample data.
  • My student Vitor Carvalho distributes the poetically named Jangada and Ciranda, which are also standalone apps built on top of Minorthird, to analyze email messages.
• SecondString is another open-source Java package, of approximate string matching techniques.
  • SecondString includes a jar for part of an ancient version of Minorthird. For those that are interested in the source behind the mysterious cls.jar, here it is.
• SLIPPER and WHIRL are now being distributed via Rutgers University. They are free for research purposes.
• To get a copy of RIPPER, please send mail to my evil twin brother, wcohen -AT- gmail.com. As an alternative to that ancient code: I haven't used it myself, but I've heard good things about J-RIP, a Ripper clone written for WEKA.

Datasets

• 617 messages from 20 Newsgroups, annotated for reply bodies and signatures, prepared by my student Vitor Carvalho
• Two subsets of the Enron data, annotated with person names, prepared by my student Einat Minkov.
• Enron email dataset (400Mb, once you get there) contains 800,000+ emails from 150 users+ organized into 4700+ folders.
• Some more email data: about two thousand messages released to the public as part of the ongoing investigation of US Attorney firings at the Dept of Justice. This is very strange data---the original email is released as scanned printouts in PDF (?!), so most of the text is not available. There are links to copies of the PDF, some manually added annotations, and a (apparently manually-reconstructed) social network graph. About 1.5Mb (in Excel format). From Mark Johnson, and a network of volunteers.
• A collection of various extraction datasets in Minorthird format (6Mb), including about 1000 Enron emails tagged for person names and temporal expressions.
• classify.tar.gz (0.4Mb) contains nine problems in which the goal is to classify short entity names. This data was used in Joins that Generalize: Text Classification Using WHIRL (KDD-98).
• ranking.tar.gz (8Mb) contains the data used for the meta-search experiments in my JAIR paper Learning to Order Things (with Rob Schapire and Yoram Singer).
• match.tar.gz (0.7Mb) contains a suite of labeled entity-name matching and clustering problems (i.e. problems for which the correct matches/clusters are provided), in a single consistent format. In most cases WHIRL's performance is given as a benchmark. (These are also distributed in the RIDDLE Repository. Extraction-oriented versions of some of this data are available on the RISE Repository. (I.e., represented as a problem of extracting data from a website, rather than matching two datasets).)

Recent talks and presentations

• Embodied Cognition and Knowledge: Integration of Heterogeneous Databases without Common Domains Using Queries Based on Textual Similarity, talk given for my 10-year "Test of Time" Award at SIGMOD-2008(Powerpoint; 11Mb)
• Using Machine Learning to Discover and Understand Structured Data, invited talk given at LinkedData 2008. (Powerpoint; 6Mb)
• Machine Learning for Personal Information Management, invited talk given at ICMLA-2007. (Powerpoint; 8Mb)
• A Framework for Learning to Query Heterogeneous Data, invited talk given at IQIS 2006. (Powerpoint; 8Mb)
• On Beyond Hypertext: Searching in Graphs Containing Documents, Words, and Actual Data, invited talk given at DB/IR Day 2006. (Powerpoint; 6Mb)
• A Century Of Progress On Information Integration: A Mid-Term Report, an overview of information integration, focusing modestly on my own work, given as invited talk at WebDB-2005. (Powerpoint; 12Mb)
• Tutorials:
  • Information extraction (PowerPoint; 4.8Mb), aimed at folks somewhat familiar with statistical NLP methods. And thanks to Thierry Poibeau, there's also a version en francais (did I get that right, Thierry?) Also, two earlier versions of this are also still around, both given with Andew McCallum at recent conferences, KDD-2003(PowerPoint; 6.8Mb) and NIPS-2002.
  • Text classification (PowerPoint; 3Mb), given at a CALD Summer Course.
  • Collaborative filtering (PowerPoint; 9.1Mb), given at a DIMACS workshop.

• A mini-course on record linkage and matching:
  • Overview of record linkage methods(PowerPoint; 250kb).
  • Overview of distance metrics for strings(PowerPoint; 530kb).
  • Overview of using HMMs for normalizing text in record linkage tasks(PowerPoint; 640kb).
    It's not a presentation, but I have also put together a short annotated bibliography of record linkage and matching papers.

• Other technical talks:
  • A presentation of my IJCAI-2005 results on "stacked sequential learning", presented in Edinburgh in August, 2005.
  • A presentation of my NIPS-2002 results on using bootstrapping techniques to improve web page classification, given at CMU in October 2002. (PowerPoint; 3.2mb).
  • A presentation of my WWW-2002 results on wrapper learning, presented in April 2002. (PDF; 170kb).
  • An overview of experiments with WHIRL. (PDF; 800kb).

Teaching

• Spring 2008: I am teaching 10-601 (Machine Learning) with Tom Mitchell, on 3-4:30 Mon & Wed in Wean Hall 5409.
• Fall 2007: Analysis of Social Media, Machine Learning 10-802 and LTI 11-772, with Natalie Glance (of Google Pittsburgh) - a brand-new seminar course. 4:30-6:30 Tuesdays in Wean Hall 4623.
• Fall 2007: Current Topics in Computational Biology (Journal Club), 02-701. (Announcements). Thursdays from 4:00-5:00 in 411 Mellon Institute (after Cell & Systems Modeling).
• Spring 2007: Information Extraction, Machine Learning 10-707 and LTI 11-748 - back by popular demand for the first time since 2004!
• Fall 2006: Current Topics in Computational Biology (Journal Club), 02-701. (Announcements)
• Spring 2006: Read the Web, CALD 10-709.
• June 21,23,25, 2005: A mini-course on Minorthird. Materials are below.
  • Slides, notes, and sample files from first day's lecture.
  • Slides, notes, and sample files from second day's lecture.
  • Powerpoint slides from third day's lecture.
  • Jar file for minorThird, if you only want to run the code, not compile it or read it. The installation process here is:
    1. Install Java 1.4 or higher (actually, JRE is all you need).
    2. Download the jar for minorThird and stick it in some directory.
    3. Optionally, download the sample data repository and unpack it into the same directory.
    4. Change to that same directory and then run Minorthird with the command
       java -Xmx500M -jar minorthird.jar

       What will pop up will be a small launch pad that can be used to start any of the UI programs. You can also start a particular main by specifying minorthird.jar as your classpath, for instance:
       java -Xmx500M -cp minorthird.jar edu.cmu.minorthird.ui.Help

  • If you want to do a real install here's the home page on Sourceforge, and a document on how to do a CVS install Minorthird.
• Spring 2004: "Learning to Turn Words into Data: Machine Learning Approaches to Information Extraction and Information Integration", CALD 10-707 and LTI 11-748.

Publications

• Here's an RSS feed of my papers, created with Dapper.
• A Computer Scientist's Guide To Biology is no longer available from this web page, but is now available from Springer. Here is a the TOC, introduction, index, and a sample chapter, from a late draft of the book; and also all the figures from the book in PowerPoint and all the figures in PDF. (The figures are a little prettier than the ones in the final book, which is black and white, not color).
• ICML 2006 Proceedings are available in print, for the true afficianado of fine learning-related research. It's well worth the money for the cover art alone (of course, all the papers are also available on-line for free.)
• Recent and selected publications. These are some representative publications for which on-line copies can be distributed.
• All publications. Here is an more-or-less complete chronological list of my publications. The bibliography includes pointers to on-line versions when I can provide them, but unfortunately copyright restrictions don't allow me to make all of my publications available on-line. Of course, reprints are always available from me on request.
• Publications by topic:
  • Matching/Data Integration
  • Text categorization
  • Information Extraction
  • Rule Learning
  • Collaborative Filtering
  • Applications
  • Formal Results
  • Inductive Logic Programming
  • Explanation-Based Learning

Students and other colleagues

• Andrew Arnold
• Ramnath Balasubramanyan
• Vitor Rocha de Carvalho
• Frank Lin
• Einat Minkov

• Richard C. Wang (co-advised with Bob Frederking)
• Noboru Matsuda (postdoc, co-supervised with Ken Koedinger)
• Tae Yano, (co-advised with Noah Smith)

• Ja-Hui Chang (visiting faculty from National Central University, Taiwan)

• Gustavo Lacerda (research assistant, co-supervised with Noboru Matsuda and Ken Koedinger)

• Zhenzhen Kou (former student, now at Yahoo!)
• Ramesh Nallapati (former postdoc, co-supervised with John Lafferty, now at Stanford)
• Edoardo Airoldi (former student, co-advised with Steve Fienberg)
• Pradeep Ravikumar (former student, co-advised with Steve Fienberg)

• I also have collaborated recently and frequently with Tom Mitchell and Bob Murphy.

Contact Info

William Cohen
Associate Research Professor
Machine Learning Department
Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213
Wean Hall 5317 / 412-268-7664 (voice) / 412-268-3431 (fax)
Assistant: Sharon Cavlovich, sharonw+@cs.cmu.edu, 412-268-5196

Official CMU Contact Info

My preferred email address is: wcohen AT cs DOT cmu DOT edu

Other Stuff

Obscure fact: two of my papers made the Citeseer's list of most-cited machine learning papers.

For those many friends whose research I have built on, be warned. My full name, "William Weston Cohen", is an anagram of the phrase "I now cite shallow men". (From Sara Cohen - no relation! - comes this warning: "Women's rights activists would probably request you to use the following anagram instead: 'I shall now cite women'".)

I am often praised for my highly artistic and functional web site designs. An example is the site for SC Indexing, a professional book indexer. However, I accept few clients - this one happens to be my wife.

Through my advisor, Alex Borgida, I can trace my "academic lineage" back to luminaries like Leibniz and Alfred Whitehead.

Poetry anyone?