Cool, we absolutely need this! I was actually about to
write something like this myself, but now I think I can
wait a little longer :-)
I have quite a few questions on this, here are just some
of them:
Can you integrate external resources in the process? For
example, I might have a list of last names, and a feature
might be if a token occurs in that list or not.
I'd like to apply this to learning for individual words
or word windows. Is that possible with/supported by your
tool?
--Thilo
Igor Sominsky wrote:
My group would like to offer the following UIMA component, Common Feature
Extractor (CFE), as an open source offering into the UIMA sandbox, assuming
there is interest from the community:
CFE enables the configuration driven feature value extraction from UIMA annotations contained in CAS. The extracted information can be used for statistical analysis, performance metrics evaluation, regression testing and machine learning related processing.
CFE provides a flexible, yet powerful language FESL (Feature Extraction
Specification Language) for working with the UIMA CAS to enable the collection
and classification of resultant data. FESL is a declarative XML-based language
that expresses semantic rules for the feature extraction. While the rules guide
the feature extraction in a completely generalized way and CFE provides methods
for subsequent processing to format the output of the extraction as needed for
downstream use. The destination for the output is defined by a particular
application where CFE is used (CAS, external file, database, etc.). CFE could
be implemented by either TAE or CAS Consumer, depending on a particular
application needs
FESL rules allow flexible and powerful way of defining multi-parameter criteria
for specific information to be extracted from CAS. Such criteria can be
customized by:
1.. a type of an UIMA annotation object that contains the feature of interest
2.. a surrounding (enclosing) annotation type and a relative location of the
object within the enclosure that limits the extraction within a boundaries of a
certain UIMA type.
3.. "path" to the feature from the annotation object
4.. a type and value of the feature itself
5.. values of any public Java get-style methods (methods that accept no
parameters and return a value) implemented by the underlying class of the
feature
6.. a location of the object or the feature on a specific path (in cases when
it is required to select/bypass annotations if they are features of other UIMA
annotation types)
The feature values can be evaluated by conditional expressions stated in FESL.
Particularly, the feature values can be evaluated whether they:
1.. are of a certain type
2.. belong to a specific set of values (vocabulary)
3.. belong to a range of numeric values (inclusively or non-inclusively)
4.. match certain bits of a bit mask (integer values only)
5.. match a Java regular expression pattern,
These expressions can be specified in disjunctive normal form that gives a
powerful and flexible way of defining fairly complex criteria for an extraction
of a required annotation and/or its value
The FESL itself is defined in XSD format and integrated with EMF for syntax validation and automated code generation.
CFE has been successfully used in several internal projects for evaluation of
performance metrics and machine learning.
CFE is described in more detail in the paper "CFE - a system for testing, evaluation and machine learning of UIMA based applications", by I. Sominsky, A. Coden, M. Tanenblatt that will be presented at UIMA for NLP workshop as part of the LREC 2008 conference in Marrakech, Morocco.
Igor Sominsky
[EMAIL PROTECTED]
