SIGIR 2010 Workshop (July 23, 2010; Geneva, Switzerland)
Overview
The Workshop on Feature Generation
and Selection for Information Retrieval will be held on July 23,
2010, in Geneva, Switzerland, in conjunction with the 33rd
Annual International ACM SIGIR Conference on Research and
Development in Information Retrieval (SIGIR 2010). The workshop
will bring together researchers and practitioners from academia
and industry to discuss the latest developments in various
aspects of feature generation and selection for textual
information retrieval.
Modern information retrieval systems facilitate information
access at unprecedented scale and level of sophistication.
However, in many cases the underlying representation of text
remains quite simple, often limited to using a weighted bag of
words. Over the years, several approaches to automatic feature
generation have been proposed (such as Latent Semantic
Indexing, Explicit Semantic Analysis, Hashing, and Latent
Dirichlet Allocation), yet their application in large scale
systems still remains the exception rather than the rule. On
the other hand, numerous studies in NLP and IR resort to
manually crafting features, which is a laborious and expensive
process. Such studies often focus on one specific problem, and
consequently many features they define are task- or
domain-dependent. Consequently, little knowledge transfer is
possible to other problem domains. This limits our
understanding of how to reliably construct informative features
for new tasks.
An area of machine learning concerned with feature generation
(or constructive induction) studies methods that endow
computers with the ability to modify or enhance the
representation language. Feature generation techniques search
for new features that describe the target concepts better than
the attributes supplied with the training instances. It is
worthwhile to note that traditional machine learning data sets,
such as those available from the UCI data repository, are only
available as feature vectors, while their feature set is
essentially fixed. In fact, feature generation for specific UCI
benchmark datasets is scorned upon. On the other hand, textual
data is almost always available in its raw format (in some case
as structured data with sufficient side information). Given the
importance of text as a data format, it is well worthwhile
designing text-specific feature generation algorithms.
Complementary to feature generation, the issue of feature
selection arises. It aims to retain only the most informative
features, e.g., in order to reduce noise and to avoid
overfitting, and is essential when numerous features are
automatically constructed. This allows us to deal with features
that are correlated, redundant, or uninformative, and hence we
may want to decimate them through a principled selection
process.
We believe that much can be done in the quest for automatic
feature generation for text processing, for example, using
large-scale knowledge bases as well as the sheer amounts of textual
data easily accessible today. We further believe the time is
ripe to bring together researchers from many related areas
(including information retrieval, machine learning, statistics,
and natural language processing) to address these issues and
seek cross-pollination among the different fields.
The workshop will include invited presentations along with presentations of accepted research
contributions. Registration will be open to all SIGIR 2010 attendees.
Keynote Speakers
Topics
Papers from a rich set of empirical, experimental, and theoretical perspectives
are invited. Topics of interest for the workshop include but are not limited to:
Identifying cases when new features should be constructed
Knowledge-based methods (including identification of appropriate knowledge resources)
Efficiently utilizing human expertise (akin to active learning, assisted feature construction)
(Bayesian) nonparametric distribution models for text (e.g. LDA, hierarchical Pitman-Yor model)
Compression and autoencoder algorithms (e.g., information bottleneck, deep belief networks)
Feature selection (L1 programming, message passing, dependency measures, submodularity)
Cross-language methods for feature generation and selection
New types of features, e.g., spatial features to support geographical IR
Applications of feature generation in IR (e.g., constructing new features for indexing, ranking)
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