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)
** 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
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.
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:
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
- 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
- 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,
- 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.
- 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
Chair and contact person:
One Microsoft Way
Redmond, WA 98052 USA
ringger AT microsoft DOT com
- 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