20.725, Calls: Lexicography/Italy; Computational Ling/USA

[LINGUIST Network]

LINGUIST List: Vol-20-725. Sat Mar 07 2009. ISSN: 1068 - 4875.

Subject: 20.725, Calls: Lexicography/Italy; Computational Ling/USA

Moderators: Anthony Aristar, Eastern Michigan U <aristar at linguistlist.org>
            Helen Aristar-Dry, Eastern Michigan U <hdry at linguistlist.org>
 
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       <reviews at linguistlist.org> 

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1)
Date: 06-Mar-2009
From: Cinzia Citraro < cinzia.citraro at libero.it >
Subject: Il Lessico come Strumento per Organizzare gli Etnosaper

2)
Date: 06-Mar-2009
From: Katrin Tomanek < katrin.tomanek at uni-jena.de >
Subject: NAACL HLT 2009 Workshop on Active Learning for NLP
 

	
-------------------------Message 1 ---------------------------------- 
Date: Sat, 07 Mar 2009 15:12:53
From: Cinzia Citraro [cinzia.citraro at libero.it]
Subject: Il Lessico come Strumento per Organizzare gli Etnosaper

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Full Title: Il Lessico come Strumento per Organizzare gli Etnosaper 

Date: 02-Jul-2009 - 04-Jul-2009
Location: Unical, Arcavacata di Rende (CS), Italy 
Contact Person: John B. Trumper Marta Maddalon
Meeting Email: clt at unical.it
Web Site: http://clt.unical.it 

Linguistic Field(s): Lexicography; Semantics 

Call Deadline: 30-Apr-2009 

Meeting Description:

Il Lessico come Strumento per Organizzare gli Etnosaper 

Call for Papers

All interested & who wish either to give a paper or attend are invited to
forward an abstract of no more than 500 words within 30/04/2009 with title,
author's affiliation, type of presentation (paper, poster or lexical note),
format *.doc, *.rtf, *.pdf. via the Conference website http://clt.unical.it. The
acts will be published.

The Conference organised by the CLT has as its theme the lexicon as an
instrument that is central to the organization & transmission of folk
knowledge.The organization of homogeneous sectors of the lexicon, lexical
learning, complex relationships between lexemes over time (long-term
etymologies), space & social divisisms. Central themes are: 
1) general lexicals problems, collection, collation & representation of such data;
2) sectorial lexicon (marginal or peripheral areas);
3) relationship between language & culture (folk knowledge in particular). 
Special theme: Padula, the 19th century & Calabrian lexicon (dialectal).



	
-------------------------Message 2 ---------------------------------- 
Date: Sat, 07 Mar 2009 15:13:05
From: Katrin Tomanek [katrin.tomanek at uni-jena.de]
Subject: NAACL HLT 2009 Workshop on Active Learning for NLP

E-mail this message to a friend:
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Full Title: NAACL HLT 2009 Workshop on Active Learning for NLP 
Short Title: ALNLP 

Date: 05-Jun-2009 - 05-Jun-2009
Location: Boulder, Colorado, USA 
Contact Person: Eric Ringger
Meeting Email: ringger at cs.byu.edu
Web Site: http://nlp.cs.byu.edu/alnlp/ 

Linguistic Field(s): Computational Linguistics 

Call Deadline: 10-Mar-2009 

Meeting Description:

NAACL HLT 2009 Workshop on
Active Learning for Natural Language Processing
June 5, 2009, Boulder, Colorado, USA
http://nlp.cs.byu.edu/alnlp/

Call for Papers

The Workshop on Active Learning for Natural Language Processing will explore the
challenges and promise of active learning for NLP tasks, including
classification, sequence labeling, parsing, semantics, and other more complex
tasks.  Both theoretical and applied research is welcome. 

Submission Deadline: extended to March 10, 2009

Endorsed by the following ACL Special Interest Groups:
- Special Interest Group on Natural Language Learning (SIGNLL)
- Special Interest Group for Annotation (SIGANN)

Motivation
Labeled data is a prerequisite for many popular algorithms in natural language
processing and machine learning.  While it is possible to obtain large amounts
of annotated data for well-studied languages in well-studied domains and
well-studied problems, labeled data are rarely available for less common
languages, domains, or problems. Unfortunately, obtaining human annotations for
linguistic data is labor-intensive and typically the costliest part of the
acquisition of an annotated corpus.

It has been shown before that active learning can be employed to reduce
annotation costs but not at the expense of quality.  While diverse work over the
past decade has demonstrated the possible advantages of active learning for
corpus annotation and NLP applications, active learning is not widely used in
many ongoing data annotation tasks.  Much of the machine learning literature on
the topic has focused on active learning
for classification problems with less attention devoted to the kinds of problems
encountered in NLP.

Topics
We are interested in bringing together researchers to explore the challenges and
opportunities of active learning for NLP tasks, language acquisition, and
language learning.  General work on active learning on NLP classification tasks,
sequence labeling, parsing, semantics, and other more complex tasks will be
welcome in the workshop.  More specific topics of interest include, but are not
limited to:

- theoretical analysis of active learning in the context of NLP applications
- novel active learning approaches to estimate the training utility of
individual selection units
- cost-sensitive active learning approaches incorporating data acquisition costs
- approaches to model or predict annotation costs as well as studies on factors
that influence annotation time
- criteria for stopping or monitoring progress of active learning
- overfitting of data acquired with active learning: how much is the data biased
towards the learning scheme involved in the selection and what are the
limitations of re-use with other learning schemes
- Human-Computer Interaction aspects of annotation including requirements,
impact of interface design on annotation time, and methods to deal with
reliability of annotators
- approaches to multi-task active learning
- approaches to deal with or reduce computational complexity of active learning
approaches including parallelization, issues of pool- or batch-size, varying
degrees of look-ahead, etc.
- active learning and domain adaption
- active learning compared to or combined with other semi-supervised or even
unsupervised learning approaches
- application of active learning in real annotation projects and experiences
gained thereby

Submissions
We invite submissions of two kinds:
1. original and unpublished work as full papers, limited to 8 pages of text (up
to one extra page may be used for references);
2. position papers or papers describing ongoing work as short papers,
limited to 4 pages in total (including references).

Both kinds of papers will appear in the proceedings and will be
presented orally.  As reviewing will be double-blind, author information
should not be included in the papers and self-reference should be avoided.

All submissions must be made in PDF format using the START paper
submission website:
https://www.softconf.com/naacl-hlt09/ActiveLearningNLP2009/

Submissions must follow the NAACL HLT 2009 formatting requirements:
http://clear.colorado.edu/NAACLHLT2009/stylefiles.html

Authors are strongly encouraged to use the LaTeX or Microsoft Word style
files available there.  Papers not conforming to these requirements are
subject to rejection without review.

Important Dates
Extended to March 10, 2009 (23:59 GMT-12): Submission Deadline
March 30, 2009: Notification of acceptance
April 12, 2009: Camera-ready copies due
June 5, 2009: Workshop held in conjunctions with NAACL HLT

Organizers and Contact
- Eric Ringger, Brigham Young University, USA
- Robbie Haertel, Brigham Young University, USA
- Katrin Tomanek, University of Jena, Germany

Please address any queries regarding the workshop to:
al.nlp2009 at googlemail.com

Program Committee
- Shlomo Argamon (Illinois Institute of Technology, USA)
- Jason Baldridge (University of Texas at Austin, USA)
- Markus Becker (SPSS, UK)
- Ken Church (Microsoft Research, USA)
- Hal Daume (University of Utah, USA)
- Robbie Haertel (Brigham Young University, USA)
- Ben Hachey (University of Edinburgh, UK)
- Udo Hahn (University of Jena, Germany)
- Eric Horvitz (Microsoft Research, USA)
- Rebecca Hwa (University of Pittsburgh, USA)
- Ashish Kapoor (Microsoft Research, USA)
- Mark Liberman (University of Pennsylvania/LDC, USA)
- Prem Melville (IBM T.J. Watson Research Center, USA)
- Ray Mooney (University of Texas at Austin, USA)
- Miles Osborne (University of Edinburgh, UK)
- Eric Ringger (Brigham Young University, USA)
- Kevin Seppi (Brigham Young University, USA)
- Burr Settles (University of Wisconsin, USA)
- Victor Sheng (New York University, USA)
- Katrin Tomanek (University of Jena, Germany)
- Jingbo Zhu (Northeastern University, China)


 





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LINGUIST List: Vol-20-725