POSTDOC(S) at STANFORD on LEARNING SEMANTICS FROM TEXT
We have postdoc opportunities for one, or possibly two, people to join
the Stanford NLP group, to work on topics in the general area of mapping
from text to meaning. Particular topics of interest include mapping
from syntactic parse trees to semantic relational structures, and from
there to semantic schemas which support probabilistic reasoning;
syntax-guided information extraction; topics in probabilistic parsing;
and applications of information extraction to educational resources.
The general approach of interest combines probabilistic models and
modern machine learning methods with a richer representational basis
than has standardly been used in statistical NLP applications. An ideal
applicant would have extensive experience in statistical approaches to
machine learning and NLP, but also some experience of rich grammar
formalisms and semantics/knowledge representation formalisms. We're
also looking for someone with good programming skills, preferably
including Java. These are grant-funded positions, and the postdoc would
need to contribute to project goals. The postdoc(s) will last for 2
years.
To apply, please send a cover letter, particularly emphasizing relevant
background, project experience, and current research interests, and your
CV, and arrange for 2 or 3 references to be sent to the address below
(post or by email). Informal inquiries can also be directed to the
address below. The position will be open until filled, but full
consideration will certainly be given to any applications received by
the end of June.
More information about NLP at Stanford can be found on the NLP Group
homepage at: http://nlp.stanford.edu/ . Stanford offers unparalleled
opportunities in computer science, and the Stanford NLP group cooperates
closely with other NLP activities at CSLI and at Berkeley.
--Christopher Manning Dept of Computer Science, Gates 4A, 353 Serra Mall, Stanford CA 94305-9040, USA
E manning@cs.stanford.edu W +1 (650) 723-7683 F +1 (650) 725-2588
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