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LINGUIST List 20.4414

Sun Dec 20 2009

Calls: Computational Ling, Text/Corpus Ling/USA

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.    Kevin Small, NAACL-HLT 2010 Workshop on Active Learning for NLP

Message 1: NAACL-HLT 2010 Workshop on Active Learning for NLP
Date: 18-Dec-2009
From: Kevin Small <kevin.smalltufts.edu>
Subject: NAACL-HLT 2010 Workshop on Active Learning for NLP
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Full Title: NAACL-HLT 2010 Workshop on Active Learning for NLP
Short Title: ALNLP

Date: 05-Jun-2010 - 06-Jun-2010
Location: Los Angeles, CA, USA
Contact Person: Kevin Small
Meeting Email: alnlp2010gmail.com
Web Site: http://www.active-learning.net/alnlp2010

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

Call Deadline: 01-Mar-2010

Meeting Description:

NAACL-HLT 2010 Workshop on Active Learning for Natural Language Processing (ALNLP)
June 5 or 6, 2010, Los Angeles, CA

The aim of the ALNLP 2010 workshop is to bring together researchers to foster
innovation and discussion that advances our understanding of practical and
theoretical issues for active learning in NLP. This workshop follows the ALNLP
workshop held in Boulder, Colorado at NAACL 2009.

Call for Papers

Submission Deadline: March 1, 2010

Labeled training data is required to achieve state-of-the-art performance for
many machine learning solutions to NLP tasks. While traditional supervised
methods rely exclusively on existing labeled data to induce a model, active
learning allows the learner to select unlabeled data for labeling in an effort
to reduce annotation costs without sacrificing performance. Thus, active
learning appears promising for NLP applications where unlabeled data is readily
available (e.g., web pages, audio recordings, minority language data), but
obtaining labels is cost-prohibitive.

Ample recent work has demonstrated the effectiveness of active learning over
a diverse range of applications. Despite these findings, active learning has
not yet been widely adopted for many ongoing large-scale corpora annotation
efforts -- resulting in a dearth of real-world case studies and copious research
questions. Machine learning literature has primarily focused on active learning
in the context of classification, devoting less attention to issues specific to
NLP including annotation user studies, incorporation of semantic information,
and more complex prediction tasks (e.g. parsing, machine translation).

The aim of this workshop is to foster innovation and discussion that advances
our understanding in these and other practical issues for active learning in
NLP. Topics of particular interest include:

- Alternative query types: labeling features rather than instances,
mixed-resolution queries for structured instances, etc.
- Creative ways for obtaining data via active learning (e.g., online games,
Mechanical Turk)
- Managing multiple, possibly non-expert annotators (e.g., "crowdsourcing"
- Reusability: using data acquired with one active learner to train other
model classes
- Domain adaptation and active learning
- Multi-task active learning
- Criteria for stopping and monitoring active learning progress
- Active learning in coordination with semi-supervised or unsupervised learning
- Interactive active learning interfaces and other HCI issues
- Parallelization of active learning and its computational challenges
- Software engineering considerations for active learning and NLP
- Theoretical analysis of active learning

We also welcome case-study papers describing the application of active
learning in real-world annotation projects and lessons learned thereby.
Additionally, we would consider papers with insights applicable to NLP from
other machine learning communities (e.g., computer vision, bioinformatics,
and data mining), where annotation costs are also high.

We invite submissions of two kinds: 1. original and unpublished work as full
papers, limited to 8 pages (+1 extra page for references); 2. position or
work-in-progress papers, limited to 4 pages (including references). Both kinds
of papers will appear in the proceedings and 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:
Submissions must follow the NAACL HLT 2010 formatting requirements:
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
March 1, 2010: Paper Submission Deadline
March 30, 2010: Notification of acceptance
June 5 or 6, 2010: Workshop held in conjunction with NAACL-HLT

Organizers and Contact
- Burr Settles, Carnegie Mellon University, USA
- Kevin Small, Tufts University, USA
- Katrin Tomanek, University of Jena, Germany

Please address any queries regarding the workshop to:

Program Committee
- Markus Becker (SPSS, UK)
- Claire Cardie (Cornell University, USA)
- Hal Daume III (University of Utah, USA)
- Ben Hachey (University of Edinburgh, UK)
- Robbie Haertel (Brigham Young University, USA)
- Udo Hahn (University of Jena, Germany)
- Eric Horvitz (Microsoft Research, USA)
- Rebecca Hwa (University of Pittsburgh, USA)
- Ashish Kapoor (Microsoft Research, USA)
- Prem Melville (IBM T.J. Watson Research Center, USA)
- Ray Mooney (University of Texas at Austin, USA)
- Fredrik Olsson (SICS, Sweden)
- Foster Provost (New York University, USA)
- Eric Ringger (Brigham Young University, USA)
- Dan Roth (University of Illinois at Urbana-Champaign, USA)
- Burr Settles (Carnegie Mellon University, USA)
- Kevin Small (Tufts University, USA)
- Katrin Tomanek (University of Jena, Germany)
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