* * * * * * * * * * * * * * * * * * * * * * * *
LINGUIST List logo Eastern Michigan University Wayne State University *
* People & Organizations * Jobs * Calls & Conferences * Publications * Language Resources * Text & Computer Tools * Teaching & Learning * Mailing Lists * Search *
* *
LINGUIST List 22.644

Tue Feb 08 2011

Calls: Computational Linguistics/UK

Editor for this issue: Amy Brunett <brunettlinguistlist.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.     Omri Abend , EMNLP 2011 Workshop on Unsupervised Learning in NLP

Message 1: EMNLP 2011 Workshop on Unsupervised Learning in NLP
Date: 01-Feb-2011
From: Omri Abend <omria01cs.huji.ac.il>
Subject: EMNLP 2011 Workshop on Unsupervised Learning in NLP
E-mail this message to a friend

Full Title: EMNLP 2011 Workshop on Unsupervised Learning in NLP
Short Title: UNSUP-2011

Date: 30-Jul-2011 - 30-Jul-2011
Location: Edinburgh, Scotland, United Kingdom
Contact Person: Omri Abend
Meeting Email: omria01cs.huji.ac.il
Web Site: http://https://sites.google.com/site/emnlpworkshop2011unsupnlp/home

Linguistic Field(s): Computational Linguistics

Call Deadline: 22-Apr-2011

Meeting Description:

In recent years, there has been an increased interest in minimizing the need for
annotated data in NLP. Significant progress has been made in the development of
both semi-supervised and unsupervised learning approaches. Although unsupervised
approaches have proved more challenging than semi-supervised ones, their further
development is particularly important because they carry the highest potential
in terms of avoiding annotation cost.

Such approaches can be applied to any language or genre for which adequate raw
text resources are available. They also bear theoretical promise for their
ability to recover novel, valuable information in textual data and to expose
underlying relations between form and various linguistic phenomena. Largely due
to these benefits, NLP has recently experienced a surge of interest in
unsupervised learning techniques. Increasingly sophisticated approaches have
been proposed and applied to a wide range of tasks, including parsing, verb
clustering, induction of grammatical categories, lexical semantics, POS tagging,
and many others.

The aim of this workshop is to bring together researchers working on different
areas of unsupervised language learning. The objective is to summarize what has
been achieved in the topic, to foster discussions on current problems in the
area, and to discuss future trends.

The workshop will be held in conjunction with EMNLP 2011 in Edinburgh, Scotland,
on the 30th of July.

Call for Papers:

Submission deadline: April 22, 2011

We welcome submissions of long and short papers and in some cases of abstracts
as well (see below for exact submission specifications) in any area or aspect of
unsupervised learning in NLP (e.g., techniques, tasks, applications, high level
issues that call for discussion), and particularly encourage submissions which
focus on the current challenges in the development and evaluation of fully
unsupervised approaches. For example:

Over the last decade several unsupervised techniques were developed and applied
to NLP (e.g. Bayesian, approximate inference, graph-based methods, and others).
However, recent methods do not always perform better than the more traditional
clustering and pattern recognition algorithms. Discussion on the contribution of
various unsupervised methods to NLP would be highly valuable.

A fundamental aim of unsupervised learning in NLP is to devise
language-independent learning mechanisms. However, languages differ greatly from
one another. What is the best way to handle language specificity in multilingual
unsupervised learning?

A prominent advantage of unsupervised learning is its ability to induce novel
information from data (e.g. new linguistic knowledge, annotation schemes, etc).
How should this information be evaluated? Is direct evaluation against an
existing gold standard a good approach? Would it be better to opt for intrinsic
(i.e., 'gold-standard independent') evaluation? Or is evaluation in the context
of another NLP task or application ideal? We welcome discussion on the pros and
cons of each method, along with novel ideas for evaluation.

The ultimate goal of unsupervised learning is to use it to aid NLP. How should
this be done, and what kind of challenges do we face when aiming to integrate
unsupervised techniques in various application tasks?


Three types of submissions will be accepted:

(1) Technical papers
(2) Position papers (perspectives/speculation)
(3) Survey papers (work done on a specific task/in a certain sub-field over a
few years).

We especially encourage submission of position and survey papers and abstracts.

Format requirements are the same as for full papers of EMNLP 2011, see
http://conferences.inf.ed.ac.uk/emnlp2011/call.html for detailed description and
style files. Submission will be electronic, using the workshop's submission
webpage at START. We accept both long and short papers in all three types of
submission. In both cases, paper length is limited to 9 pages of content and any
number of additional pages with references only. We also welcome the submission
of abstracts (up to one page) of position and survey papers (but not of
technical papers). Abstracts should be formatted using the EMNLP style files and
submitted as a separate file (just like short and long papers). Short papers and
abstracts will neither be favored nor disfavored in the review process.


The reviewing of the papers will be blind. The paper should not include the
authors' names and affiliations.Furthermore, self citations and other references
(e.g. to projects, corpora, or software) that could reveal the author's identity
should be avoided. For example, instead of 'We previously showed (Smith, 1991)',
write 'Smith previously showed (Smith, 1991)'.

Important Dates:

April 22, 2011 Due date for submissions
May 20, 2011 Notification of acceptance
June 3, 2011 Deadline for final camera-ready version
July 30, 2011 Workshop


Omri Abend (Hebrew University of Jerusalem,omria01cs.huji.ac.il)
Anna Korhonen (University of Cambridge, alk23cam.ac.uk)
Ari Rappoport (Hebrew University of Jerusalem, arircs.huji.ac.il)

Roi Reichart (MIT, roiricsail.mit.edu)

Program Committee:

Eneko Agirre (University of the Basque Country, Spain)
Jason Baldridge (University of Texas at Austin, USA)
Tim Baldwin (University of Melbourne, Australia)
Sam Brody (Columbia University, USA)
Alexander Clark (Royal Holloway, University of London, UK)
Shay Cohen (Carnegie Mellon University, USA)
Mona Diab (Columbia University, USA)
Gregory Druck (University of Massachusetts Amherst, USA)
Jason Eisner (Johns Hopkins University, USA)
Sharon Goldwater (University of Edinburgh, UK)
Joao Graca (University of Pennsylvania, USA)
Ioannis Klapaftis (University of York, UK)
Lillian Lee (Cornell University, USA)
Percy Liang (UC Berkeley, USA)
Diana McCarthy (Lexical Computing, Ltd., UK)
Preslav Nakov (National University of Singapore, Singapore)
Roberto Navigli (University of Rome, Italy)
Vincent Ng (UT Dallas, USA)
Ted Pedersen (University of Minnesota, USA)
Andrew Rosenberg (CUNY, USA)
Sabine Schulte im Walde (University of Stuttgart, Germany)
Valentin Spitkovsky (Stanford University, USA)
Carlo Strapparava (FBK-irst, Italy)
Ben Taskar (University of Pennsylvania, USA)
Kristina Toutanova (Microsoft Research, USA)
Andreas Vlachos (University of Wisconsin-Madison, USA)
Read more issues|LINGUIST home page|Top of issue

Page Updated: 08-Feb-2011

Supported in part by the National Science Foundation       About LINGUIST    |   Contact Us       ILIT Logo
While the LINGUIST List makes every effort to ensure the linguistic relevance of sites listed on its pages, it cannot vouch for their contents.