CALL FOR PAPERS
Feature
Engineering for Machine Learning in Natural Language Processing
Workshop at the Annual
Meeting of the Association of
SUBMISSION DEADLINE:
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 of NLP 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.
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.
SUBMISSION INSTRUCTIONS
The language of the workshop is English. Submitted papers should be prepared in PDF
format (all fonts included) or Microsoft Word .doc format and not longer than 8
pages following the
Submissions should be sent as an attachment to the
following email address:
ringger AT
microsoft DOT com .
In the body of the submission email, please include the following identification
information:
Title
Author(s) name(s), affiliation(s), and e-mail
address(es)
Abstract: short summary (up to 5 lines)
The papers themselves should contain no identifying
information, and reviewing will be conducted in a blind fashion.
All accepted papers will be presented in during the
workshop and collected in the printed proceedings.
IMPORTANT DATES
Paper submission deadline:
Notification of acceptance:
Submission of camera-ready copy:
Workshop:
ORGANIZATION
Chair
and contact person:
Microsoft Research
Program
Committee: