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LINGUIST List 18.1825

Sun Jun 17 2007

Confs: Cognitive Sci,Computational Ling,Psycholinguistics/Germany

Editor for this issue: Jeremy Taylor <jeremylinguistlist.org>

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        1.    Shravan Vasishth, Fall School in Computational Linguistics

Message 1: Fall School in Computational Linguistics
Date: 17-Jun-2007
From: Shravan Vasishth <vasishthacm.org>
Subject: Fall School in Computational Linguistics

Fall School in Computational Linguistics

Date: 03-Sep-2007 - 14-Sep-2007
Location: Potsdam, Germany
Contact: Shravan Vasishth
Contact Email: vasishthacm.org
Meeting URL: http://www.ling.uni-potsdam.de/fallschool/

Linguistic Field(s): Cognitive Science; Computational Linguistics;
Psycholinguistics; Text/Corpus Linguistics

Meeting Description:

The Computational Linguistics Fall School was established by the Deutsche
Gesellschaft fuer Sprachwissenschaft (German Linguistics Association,
www.dgfs.de) as a biennial event for students who wish to widen their knowledge
of techniques and methods used in natural language processing.

The next Fall School will take place 3-14 September, 2007, in Potsdam, at the
Institute for Linguistics, University of Potsdam, Germany.

Early registration deadline is June 30, 2007. Registration fees before June 30,
2007 are 125€ (student) and 175€ (non-student). Registration after June 30, 2007
, is 200€ and 250€ respectively. There are only 25 seats available due to
computer pool constraints, so apply as early as possible.

The Fall School website is: www.ling.uni-potsdam.de/fallschool/.

Funding: some fee waivers and other funding are available to students. If you
would like to apply for financial assistance, please send a brief CV and a brief
(one page) statement of interest to Shravan Vasishth at vasishthacm.org. If you
have already applied for funding, there is no need to do so again.

For previous fall school courses see the website for the CL section of the
German Linguistic Association: www.dgfs.de/cgi-bin/dgfs.pl/coli

Lecturers and courses

1. Amit Dubey: Statistical Parsing: from theory to engineering, and engineering
to cognitive modeling

Computational linguists have a wide variety of tools at their
disposal. This course serves as a brief introduction to some of these
tools as applied to statistical parsing. Syntactic analysis provides a
insightful overview of computational methods as parsing is such a
foundational aspect of language understanding. In addition to an
summary of technical and engineering aspects, this course will also
touch on cognitive modeling applications of parsing research. The
major topics of the course are:

- Basic probability theory
- Algorithms for parsing
- Picking the right analysis: probabilistic versus ''discriminative'' approaches
- Grammar theories and parsing: phase-structure, attribute-value and dependency
- Poverty of stimulus revisited: grammar induction
- Psycholinguistic modeling

2. Stefan Evert and Marco Baroni: Statistical programming in R for computational

This course is aimed at students who have a basic knowledge of
statistics, and introduces various statistical and data exploration
techniques of interest to computational linguists in a hands-on
fashion, using the open-source software package R

Topics covered include:

- basic data manipulation in R
- R graphics
- classical hypothesis tests and statistical analysis
- statistical inference for count data: frequency comparison and collocations
- generalised linear models, with emphasis on logistic regression
- exploratory multivariate analysis
- clustering and machine learning techniques
- word frequency distributions: the zipfR package

Throughout the course, emphasis will be on the practical analysis of
real linguistic data sets, rather than on theoretical aspects of the
statistical methods (although we will discuss the logic behind each
technique, as well as typical problem cases where linguistic data do
not meet the assumptions made by statistical models).

3. Rainer Osswald: Ontologies and Lexical Semantic Resources

Ontological and lexical semantic resources are widely used in natural
language understanding and information processing tasks. This course
will give an introduction to various lexical semantic resources
including WordNet, FrameNet, VerbNet, SIMPLE, and the LCS database as
well as to top-level and mid-level ontologies such as DOLCE, SUMO,
GUM, and Mikrokosmos. The theoretical assumptions and the design
principles underlying these resources will be thoroughly discussed and
compared. The course will also be concerned with possible interactions
and alignments between ontological and lexical semantic
resources. Further topics are data formats, markup frameworks, and
tools for supporting the construction and maintenance of the
resources. Finally, we will briefly look at corpus-based techniques
for automatically constructing and extending ontologies and lexical
semantic resources. The course presumes a basic acquaintance with
lexical semantics and elementary logic.

4. Manfred Stede and Stefanie Dipper: Text Structure and Text Understanding

What is it that turns a sequence of sentences into a meaningful,
reasonable, coherent text? In this course, we investigate the central
levels of description for text coherence and relate them to
computational applications:

- the range of coreference phenomena and the task of anaphora resolution;
- the role of intentions and rhetorical relations, and the notion of
''rhetorical parsing'';
- thematic structure of text and sentence;
- the influence of genre and text type, and its automatic identification.

Participants will work with sample texts and produce analyses on the
levels discussed. Also, several automatic analysis modules will be

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