Claire Cardie
Professor, Department of Computer Science
          and Department of Information Science
Cornell University
417 Gates Hall

Phone: 607-255-9206
Fax: 607-255-4428
Email: cardie at cs dot cornell dot edu 
Administrative assistant: Randy Hess (rbhess at cs dot cornell dot edu) 

I am on sabbatical for the 2014--2015 academic year.

Research Interests
Teaching
Publications
CV/Resume 

Code release (April 2010): The Reconcile Research Platform for Noun Phrase Coreference Resolution
 
Organizer of the TAC KBP 2014 Sentiment Track.
 
Organizer (with Carmen Banea, Rada Mihalcea and Janyce Wiebe) of the SemEval-2014 task on Semantic Textual Similarity in Spanish.
 
Current (or very recent) conference program committees:

Please submit papers to, and attend these conferences :).

I was also program chair of the joint ACL-COLING 2006 conference with Pierre Isabelle.

Other current and recent activities:
NLP and ML Links
Research Interests

My primary research is in the area of natural language processing (NLP) where our goal is to develop algorithms and systems that will vastly improve a user's ability to find, absorb, and extract information from on-line text. My group's research generally proceeds at two complementary levels: we focus both on building real systems for large-scale natural language processing tasks and on developing techniques to address underlying theoretical problems in the syntactic, semantic and pragmatic analysis of natural language. As has become more or less standard in the field, we rely on statistical machine learning techniques as our primary modeling tool, both for guiding natural language system development and for exploring the mechanisms that underlie language understanding. Our current work encompasses a number of related areas:

With colleagues from the Information Science program, I also work on:
Recent Teaching
Selected Publications See the acknowledgment sections of individual papers for specific funding support information. Any opinions, findings, and conclusions or recommendations expressed in these publications or on this web site are those of the author(s) and do not necessarily reflect the views of any funding agencies that support our work.
NLP and Machine Learning Links