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SUPPORT-VECTOR-MACHINES  2005

SUPPORT-VECTOR-MACHINES 2005

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Subject:

CFP: ACL 2005 Workshop on Feature Engineering for Machine Learning in NLP

From:

Kevin Duh <[log in to unmask]>

Reply-To:

The Support Vector Machine discussion list <[log in to unmask]>

Date:

Wed, 2 Mar 2005 20:46:22 -0800

Content-Type:

TEXT/PLAIN

Parts/Attachments:

Parts/Attachments

TEXT/PLAIN (135 lines)

----------------------------------------------------------------

                             CALL FOR PAPERS

                 Feature Engineering for Machine Learning
                      in Natural Language Processing

                    Workshop at the Annual Meeting of
         the Association of Computational Linguistics (ACL 2005)

   http://research.microsoft.com/~ringger/FeatureEngineeringWorkshop/

                ** Submission Deadline: April 20, 2005 **


                           Ann Arbor, Michigan
                              June 29, 2005

----------------------------------------------------------------

As experience with machine learning for solving natural language
processing tasks accumulates in the field, practitioners are finding
that feature engineering is as critical as the choice of machine
learning algorithm, if not more so.  Feature design, feature
selection, and feature impact (through ablation studies and the like)
significantly affect the performance of systems and deserve greater
attention.  In the wake of the shift away from knowledge engineering
and of the successes of data-driven and statistical methods,
researchers in the field are likely to make further progress by
incorporating additional, sometimes familiar, sources of knowledge as
features.  Although some experience in the area of feature engineering
is to be found in the theoretical machine learning community, the
particular demands of natural language processing leave much to be
discovered.

This workshop aims to bring together practitioners of NLP, machine
learning, information extraction, speech processing, and related
fields with the intention of sharing experimental evidence for
successful approaches to feature engineering, including feature design
and feature selection.  We welcome papers that address these goals.
We also seek to distill best practices and to discover new sources of
knowledge and features previously untapped.

The workshop will include an invited talk by Andrew McCallum of the
University of Massachusetts at Amherst.


SUBMISSION

Submitted papers should be prepared in PDF format (all fonts included)
or Microsoft Word .doc format and not longer than 8 pages following
the ACL style.  More detailed information about the format of
submissions can be found here:

    http://www.aclweb.org/acl2005/index.php?stylefiles

The language of the workshop is English.  Submissions should be sent
as an attachment to the following email address: ringger AT microsoft
DOT com .  All accepted papers will be presented in oral sessions of
the workshop and collected in the printed proceedings.

Submissions are invited on all aspects of feature engineering for
machine learning in NLP.  Topics may include, but are not necessarily
limited to:

- Novel methods for discovering or inducing features, such as mining
    the web for closed classes, useful for indicator features.

- Comparative studies of different feature selection algorithms for
    NLP tasks.

- Interactive tools that help researchers to identify ambiguous cases
    that could be disambiguated by the addition of features.

- Error analysis of various aspects of feature induction, selection,
    representation.

- Issues with representation, e.g., strategies for handling
    hierarchical representations, including decomposing to atomic
    features or by employing statistical relational learning.

- Techniques used in fields outside NLP that prove useful in NLP.

- The impact of feature selection and feature design on such practical
    considerations as training time, experimental design, domain
    independence, and evaluation.

- Analysis of feature engineering and its interaction with specific
    machine learning methods commonly used in NLP.

- Combining classifiers that employ diverse types of features.

- Studies of methods for defining a feature set, for example by
    iteratively expanding a base feature set.

- Issues with representing and combining real-valued and categorical
    features for NLP tasks.


IMPORTANT DATES

- Paper submission deadline:        April 20, 2005; Noon, PST (GMT-8)

- Notification of acceptance:       May 10, 2005

- Submission of camera-ready copy:  May 17, 2005

- Workshop:                         June 29, 2005


ORGANIZATION

Chair and contact person:

        Eric Ringger
        Microsoft Research
        One Microsoft Way
        Redmond, WA 98052  USA
        ringger AT microsoft DOT com


Program Committee:

- Simon Corston-Oliver, Microsoft Research, USA
- Kevin Duh, University of Washington, USA
- Matthew Richardson, Microsoft Research, USA
- Oren Etzioni, University of Washington, USA
- Andrew McCallum, University of Massachusetts at Amherst, USA
- Dan Bikel, IBM Research, USA
- Olac Fuentes, INAOE, Mexico
- Chris Manning, Stanford University, USA
- Kristina Toutanova, Stanford University, USA
- Hideki Isozaki, NTT Communication Science Laboratories, Japan
- Caroline Sporleder, University of Edinburgh, UK

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