Dear all,
This is to announce the release 1.1 of Shalmaneser (a SHALlow seMANtic
parSER), a system for automatic sense assignment and semantic role
labeling. Shalmaneser comes with pre-trained FrameNet classifiers for
English and German. (Further details below.)
Improvements over the previous release (1.0) include:
- Pre-trained English classifiers for FrameNet release 1.3
(i.e., improved coverage and accuracy)
- Support for TreeTagger as POS Tagger for both English and German
- Removal of numerous bugs
You can download the software from this URL:
http://www.coli.uni-saarland.de/projects/salsa/shal/
Again, please let us know if you have any questions, comments, or
suggestions!
Best,
Sebastian Pado and Katrin Erk
--- Purpose of ShalmaneserShalmaneser has been developed with two uses in mind: research in applications that use shallow semantic parses, and research on better shallow semantic parsing. So Shalmaneser can be used either as a 'black box' to obtain semantic parses for free text, or as a research platform that can be extended to new parsers, languages, or classification paradigms.
Features of Shalmaneser
- Shallow semantic parser: word sense disambiguation for predicates, plus semantic role labeling - Input: plain text. Syntactic processing integrated. - Classifiers available: trained on FrameNet data for English and German (System also applicable to other frameworks) - System output can be viewed graphically in the SALTO viewer: http://www.coli.uni-saarland.de/projects/salsa/page.php?id=software - System realized as a toolchain of independent modules communicating through a common XML format, hence extensible by further modules - Interfaces for addition/exchange of parsers, learners, features
More information
K. Erk and S. Pado: Shalmaneser - a flexible toolbox for semantic role assignment. Proceedings of LREC-06, Genoa. http://www.coli.uni-saarland.de/~pado/pub/papers/lrec06_erk.pdf
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