Corpora: job suitable for statistical computational linguist

From: James Cussens (jc@cs.york.ac.uk)
Date: Tue Jun 13 2000 - 11:19:07 MET DST

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    Dear folks,

    It may well be that you know someone (perhaps yourself) suitable for
    the following post. A statistically inclined computational linguist
    would fit the bill - something I did not stress sufficiently in the
    advert.

    James

    James Cussens jc@cs.york.ac.uk
    Department of Computer Science Tel +44 (0)1904 434732
    University of York Fax +44 (0)1904 432767
    Heslington, York YO10 5DD, UK http://www.cs.york.ac.uk/~jc

    **********************************************************************

        POSTDOCTORAL RESEARCHER AT DEPARTMENT OF COMPUTER SCIENCE
                              UNIVERSITY OF YORK

                    Induction of Stochastic Logic Programs

    **********************************************************************

    James Cussens has recently been awarded an EPSRC-funded 1-year
    research grant starting 1st Oct 2000 to investigate methods for
    inducing stochastic logic programs. A postdoctoral post is therefore
    available within the Artificial Intelligence group.

    This post requires a post-doctoral researcher with a background in
    statistical analysis of complex models. Ideally this would be combined
    with experience of logic programming. The post would suit someone
    interested in extending existing probabilistic models such as Bayes
    nets and probabilistic context-free grammars. Knowledge of statistical
    computational linguistics would be an advantage as would some
    familiarity with inductive logic programming.

    The appointment is for a period of one year starting 1st October 2000.
    Funds on the grant allow us to appoint at the maximum point on the
    Grade IA scale (24,479 GBP per annum).

    It is expected that James will be working with David Page (Departments
    of Computer Science and Biostatistics and Medical Informatics) at the
    University of Wisconsin for the latter half of the project. The
    researcher appointed on this project should be able to join James on
    this visit should they wish to do so.

    Informal enquiries may be made to: James Cussens (jc@cs.york.ac.uk,
    Tel: +44 1904 434732).

    MORE INFORMATION ABOUT THE PROJECT CAN BE FOUND AT
    http://www.cs.york.ac.uk/~jc/research/slps/

    CLOSING DATE FOR APPLICATIONS WILL BE 12 JULY 2000.
    Interviews are expected to be on 3rd August.

    Formal applications can be made by sending THREE copies of a letter of
    application and a full curriculum vitae, together with the names and
    addresses of three referees, to the Personnel Office, University of
    York, Heslington, York YO10 5DD, UK. Please quote reference number
    6035. Email applications will NOT be accepted.

    Overview
    --------

    Probabilistic models are currently an important focus of research in
    both Artificial Intelligence and Computational Linguistics. In the AI
    community, attention has focussed on Bayesian nets and related
    graphical models. In computational linguistics n-gram models, hidden
    Markov models and stochastic context-free grammars have been used
    widely as part of the `statistical natural language processing
    revolution.'

    In both communities there is interest in extending current methods to
    incorporate domain knowledge (e.g. linguistic knowledge) and/or
    relational data. Inductive logic programming is an approach to machine
    learning that does allow domain knowledge to be incorporated, but
    where probabilistic methods are relatively undeveloped.

    Stochastic logic programs (SLPs) are logic programs with added
    parameters which define a log-linear distribution over proofs. It has
    previously been argued that they effectively combine many of the
    strengths of statistical and logical models. The project intends to
    put this argument to the test by combining and developing work in both
    computational linguistics and uncertainty in AI with the goal of
    designing and implementing algorithms for learning both structure and
    parameters of SLPs from data and background knowledge.

    Department
    ----------
    The Department of Computer Science has a record of high achievement in
    research and teaching. It was rated Grade 5* (this is the highest
    possible rating i.e. attainable levels of international excellence in
    a majority of sub-areas of activity and to attainable levels of
    national excellence in all others) in the 1996 Research Assessment
    Exercise, and Excellent (i.e. demonstrably very high levels of
    achievement and best practice) in the 1994 Teaching Quality Assessment
    Exercise. The University won the 1996 Queen's Anniversary Prize for
    Higher and Further Education for the work of the Department of
    Computer Science, and a recent ranking of UK Computer Science
    departments placed the department equal first in a ranking of 83
    institutions.

    The Department's continued expansion and new facilities are
    accommodated in a purpose-built, 4200 square-metre building, opened in
    September 1997, which occupies the highest point on campus. The most
    prominent of the Department's facilities is a 1.6 million pound
    Silicon Graphics Origin-2300 supercomputer with 32 processors and 8
    gigabytes of memory. This provides one of the best resources for high
    performance computing at a UK academic institution.

    The Artificial Intelligence Group is one of the largest and most
    productive in the UK. The research of the Group is concerned with the
    theoretical principles of artificial intelligence and their
    application to real-world domains. Most of the Group's research focuses
    on the areas of automated reasoning, machine learning, natural language
    processing and intelligent user interfaces.



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