Chris Manning works on systems and formalisms that can intelligently process and produce human languages. His research concentrates on probabilistic models of language and statistical natural language processing, information extraction, text understanding and text mining, constraint-based theories of grammar (HPSG and LFG) and probabilistic extensions of them, syntactic typology, computational lexicography (involving work in XML, XSL, and information visualization), and other topics in computational linguistics and machine learning. Together with Dan Klein, he received the ACL 2003 best paper award.
| M | Dept of Computer Science, Gates Building 1A, 353 Serra Mall, Stanford CA 94305-9010, USA |
| E | manning@cs.stanford.edu |
| W | +1 (650) 723-7683 |
| F | +1 (650) 725-1449 |
| R | Gates 158 |
| O | Tu 4-5, Wed 2-3 |
| A | Debbie Barros, Gates 150, +1 (650) 725-3358, dbarros@cs.stanford.edu |
Most of my papers are available online in my publication list. Here's a reasonable approximation of me on Google Scholar.
My newest book project, with Hinrich Schütze and Prabhakar Raghavan, is an Introduction to Information Retrieval. My "bestseller" is Manning and Schütze, Foundations of Statistical Natural Language Processing (MIT Press, 1999). My two monographs are Ergativity: Argument Structure and Grammatical Relations and Complex Predicates and Information Spreading in LFG.
A few talks are available online.
My current research focuses on robust but linguistically sophisticated probabilistic natural language processing, and opportunities to use it in real-world domains. Particularly topics include richer models for probabilistic parsing, grammar induction, text categorization and clustering, incorporating probabilistic models into constraint-based syntactic theories such as Head-driven Phrase Structure Grammar and Lexical Functional Grammar, electronic dictionaries and their usability, particularly for indigenous languages, information extraction and presentation, and linguistic typology.
My research at Stanford is currently supported by an IBM Faculty Partnership Award, ARDA, Scottish Enterprise, and DARPA. Previous funding at Stanford comes from a Terman Fellowship, NSF (for GIB), NTT, NHK, and the Australian Reseach Council.
I am interested in new students wanting to work in the area of Natural Language Processing. To find out more about what I do, it's best to look at my papers, or my group research page. However, to cut down on my email load, it's necessary to put in some more information:
In fall 2007 I will again teach Ling 289: Quantitative and Probabilistic Explanation in Linguistics MW 2:15-3:45 in 160-318. I previously taught it in winter 2002 (née Ling 236) and Winter 2005 (as Ling 235).
In the summer of 2007, I taught at the LSA Linguistic Institute: Statistical Parsing and Computational Linguistics in Industry.
In Fall 2002 and Winter 2003, I taught a new sequence of courses on Information Retrieval and text information extraction, broadly construed ("IR++"), CS276A/B, with Prabhakar Raghavan and Hinrich Schütze. Fall quarter course website. Winter quarter course website. CS276B in winter quarter will focus on text classification, text clustering, information extraction, summarization, event detection, and text mining, and textual applications in bioinformatics. Hinrich and Prabhakar will teach CS276 in 2003-04.
In spring 2005, I will again teach CS 224N / Ling 237. Natural Language Processing -- Develops an in-depth understanding of both the algorithms available for the processing of linguistic information and the underlying computational properties of natural languages. Morphological, syntactic, and semantic processing from both a linguistic and an algorithmic perspective. Focus on modern quantitative techniques in NLP: using large corpora, statistical models for acquisition, disambiguation, and parsing. Examination and construction of representative systems. Prerequisites: 121/221 or Ling 138/238, and programming experience. Recommended: basic familiarity with logic and probability. 3 units. The required text is Christopher Manning and Hinrich Schütze, Foundations of Statistical Natural Language Processing. See below. As an additional optional text, you will also find in the bookstore Jurafsky and Martin, Speech and Language Processing. I've taught this course yearly Spr 2000-2003. Many previous student projects are available online.
In fall 1999 and winter 2001, I taught CS 121 Artificial Intelligence. The text book was S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach.
The NLP Reading Group (which I ran 1999-2002) has now been transformed into The Natural Language and Speech Processing Colloquium.
I've now got a Treo 650 and am enjoying being able to get email everywhere. But it meant I desperately needed spam filtering happening on the mail server. Here are instructions on how to set that up at Stanford. For using the web, here are some some sites for PDA browsing.
LaTeX: When I used to have more time (i.e., when I was a grad student), I used to spend some of it writing (La)TeX macros. [Actually, that's a lie; I still spend some time doing it....]
We've now got two sons: Joel and Casey. Here are my opinions on books for the very young.
Me in the Stanford Report: appointment, Terman, csli, lexicography, database, itr 2000, AAAS 2001, on NewsBlaster, childcare, PageRank speed-ups, reappointment.
http://www.stanford.edu/~manning/