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LINGUIST List 21.1132

Mon Mar 08 2010

Calls: Computational Ling, Psycholing/Sweden

Editor for this issue: Kate Wu <katelinguistlist.org>

LINGUIST is pleased to announce the launch of an exciting new feature: Easy Abstracts! Easy Abs is a free abstract submission and review facility designed to help conference organizers and reviewers accept and process abstracts online. Just go to: http://www.linguistlist.org/confcustom, and begin your conference customization process today! With Easy Abstracts, submission and review will be as easy as 1-2-3!
        1.    Preslav Nakov, ACL 2010 Workshops: SemEval-2010

Message 1: ACL 2010 Workshops: SemEval-2010
Date: 06-Mar-2010
From: Preslav Nakov <preslavngmail.com>
Subject: ACL 2010 Workshops: SemEval-2010
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Full Title: ACL 2010 Workshops: SemEval-2010

Date: 15-Jul-2010 - 16-Jul-2010
Location: Uppsala, Sweden
Contact Person: David Traum
Meeting Email: workshopsacl2010.org
Web Site: http://acl2010.org/workshops.html

Linguistic Field(s): Computational Linguistics; Psycholinguistics

Call Deadline: 02-Apr-2010

Meeting Description:

Cognitive Modeling and Computational Linguistics (CMCL) and TopiCS special issue
Models of Language Comprehension
July 15th, 2010

A workshop to be held following the Association for Computational Linguistics
meeting in Uppsala, Sweden

Second Call for Papers

SemEval-2010 Shared Task #8:
Multi-Way Classification of Semantic Relations
Between Pairs of Nominals

Training data available

This shared task should be of interest to researchers working on
- semantic relation extraction
- information extraction
- lexical semantics

Recently, the NLP community has shown a renewed interest in deeper semantic
analysis, including automatic recognition of semantic relations between pairs of
words. This is an important task with many potential applications in Information
Retrieval, Information Extraction, Text Summarization, Machine Translation,
Question Answering, Paraphrasing, Recognizing Textual Entailment, Thesaurus
Construction, Semantic Network Construction, Word Sense Disambiguation, and
Language Modelling.

Despite this interest, progress was slow due to the incompatibility of the
different classification schemes proposed and used, which made it difficult to
compare the various classification algorithms. Most of the datasets used so far
provided no context for the target relation, thus relying on the assumption that
semantic relations are largely context-independent, which is not a realistic
assumption. A notable exception is SemEval-2007 Task 4: Classification of
Semantic Relations between Nominals, which for the first time provided a
standard benchmark dataset for seven semantic relations - in context- . However,
in order to avoid the challenge of defining a single unified standard
classification scheme, this dataset treated each semantic relation separately,
as a single two-class (positive vs. negative) classification task, rather than
as multi-way classification. While some subsequent publications tried to use the
dataset in a multi-way setup, it was not designed to be used in that manner.

We believe that having a freely available standard benchmark dataset for -
multi-way- semantic relation classification - in context- is much needed for
the overall advancement of the field. Thus, we have posed as our primary
objective the challenging task of preparing and releasing such a dataset to the
research community. We further set up a common evaluation task that will enable
researchers to compare their algorithms.

The Task
Task: Given a sentence and two annotated nominals, choose the most suitable
relation from the following inventory of nine relations:

- Relation 1 (Cause-Effect)
- Relation 2 (Instrument-Agency)
- Relation 3 (Product-Producer)
- Relation 4 (Content-Container)
- Relation 5 (Entity-Origin)
- Relation 6 (Entity-Destination)
- Relation 7 (Component-Whole)
- Relation 8 (Member-Collection)
- Relation 9 (Message-Topic)

It is also possible to choose Other if none of the nine relations appears to be

Example: The best choice for the following sentence would be Component-Whole(e1,e2):

"The macadamia nuts in the cake also make it necessary to have
a very sharp knife to cut through the cake neatly."

Note that in the above sentence, Component-Whole(e1,e2) holds, but
Component-Whole(e2,e1) does not, i.e., we have Other(e2,e1). Thus, the task asks
for determining - both- the relation and the order of e1 and e2 as its arguments.

- Training Dataset: The training dataset consists of a total of 8,000 examples.

- Test Dataset: The test dataset consists of over 2,717 examples; it will be
released on March 18.

License: All data are released under the Creative Commons Attribution 3.0
Unported license.

Time Schedule
- Trial data released: August 30, 2009
- Training data released: March 5, 2010
- Test data release: March 18, 2010
- Result submission deadline: 7 days after downloading the - test- data, but
no later than April 2

- Organizers send the test results: April 10, 2010
- Submission of description papers: April 17, 2010
- Notification of acceptance: May 6, 2010
- SemEval'2010 workshop (at ACL): July 15-16, 2010

Task Organizers
Iris Hendrickx. University of Lisbon, University of Antwerp
Su Nam Kim. University of Melbourne
Zornitsa Kozareva. University of Southern California, Information Sciences Institute
Preslav Nakov. National University of Singapore
Diarmuid Ó Séaghdha. University of Cambridge
Sebastian Padó. Stuttgart University
Marco Pennacchiotti. Saarland University, Yahoo! Research
Lorenza Romano. FBK-irst, Italy
Stan Szpakowicz. University of Ottawa

Useful Links
Interested in participating in the shared task? Please join the following Google

Task #8 website: http://docs.google.com/View?docid=dfvxd49s_36c28v9pmw

SemEval 2010 website: http://semeval2.fbk.eu/semeval2.php
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