Teg Grenager

Real Name: Trond Erling Grenager (TEG is my initials)
Office: Gates Building, Room 232
Email: [lastname] at [university] dot edu

I am a fifth-year Ph.D. student in the Stanford University Department of Computer Science. I'm currently on leave from Stanford founding a new video advertising company, AdapTV.

In my academic life I study statistical natural language processing and machine learning with Professor Chris Manning. Recently, my research interests have focused on learning models of linguistic structure using unsupervised and semi-supervised machine learning techniques. I am motivated by the idea that all the text and information on the web ought to be able to help us build better models of language. Don't ask me why, but I like the idea of robots that learn about the world by reading and talking to us!

Courses

I taught CS121, Introduction to Artificial Intelligence, at Stanford in the summer of 2006.

Publications

Trond Grenager and Christopher D. Manning (2006) Unsupervised discovery of a statistical verb lexicon. EMNLP 2006 [PS] [PDF]
Bill MacCartney, Trond Grenager, Marie-Catherine de Marneffe, Daniel Cer, Christopher D. Manning (2006) Learning to recognize features of valid textual entailments. NAACL 2006 [PDF]
Trond Grenager, Dan Klein, and Christopher D. Manning (2005) Unsupervised learning of field segmentation models for information extraction. ACL 2005. [PS] [PDF]
Jenny Finkel, Trond Grenager, and Christopher D. Manning (2005) Incorporating non-local information into information extraction systems by Gibbs sampling. ACL 2005. [PS] [PDF]
Galen Andrew, Trond Grenager, and Christopher D. Manning (2004) Verb sense and subcategorization: using joint inference to improve performance on complementary tasks. EMNLP 2004. [PS] [PDF]
Yoav Shoham, Robert Powers, and Trond Grenager (2004) On the Agenda(s) of Research on Multi-Agent Learning. 2004 AAAI Fall Symposium on Artificial Multi-Agent Learning. [PS] [PDF]
Trond Grenager, Robert Powers, and Yoav Shoham (2002) Dispersion games: general definitions and some specific learning results. AAAI 2002. [PS] [PDF]
K. Saito, P. Langley, T. Grenager, C. Potter, A. Torregrosa, and S.A. Klooster (2001) Computational revision of quantitative scientific models. Conf. on Discovery Science 2001. [PS] [PDF]

Presentations/Tutorials

Tutorial on Partially Observable Markov Decision Processes (POMDPs) [PPT]
Tutorial on Probabilistic Relational Models (PRMs) [PPT]
Tutorial on Markov Chain Monte Carlo methods [PPT]
Tutorial on the Dirichlet Process for Mixture Modeling [PPT]
Class on clustering methods in NLP that I taught as part of CS294-5 at Berkeley. [PDF] [PPT]

Data

The unsupervised field segmentation data lives here.

Consulting

When time permits, I apply machine learning and natural language processing techniques to commercial problems in the form of software and consulting projects through my firm: Talking Dolphin, LLC.