Abstract of FKI-191-94

Document-Name:  fki-191-94.ps.gz
Title:		Extracting Semantic Features for Aspectual Meanings 
		from a Syntactic Representation Using Neural Networks
Authors:	Gabriele Scheler 
Revision-Date:	1994/05/05
Category:	Technical Report (Forschungsberichte Künstliche Intelligenz)
Abstract:	The main point of this paper is to show how we can extract 
		semantic features, describing aspectual meanings, from
		a syntactic representation.
		The goal is to translate English to Russian aspectual 
		categories.
		This is realized by a specialized language processing module, 
		which is based on the concept of vertical modularity.
		The results of supervised learning of syntactic-semantic 
		correspondences using standard back-propagation show that 
		both learning and generalization to new patterns is successful.
		Furthermore, the correct generation of Russian aspect from 
		the automatically created semantic representations is 
		demonstrated.
Keywords:	interpretation of written language, machine translation, 
		grammar checker, grammatical aspect, vertical modularity, 
		semantic representation, syntactic representation, 
		connectionist NLP
Size:		16 pages
Language:	English
ISSN:		0941-6358
Copyright:	The ``Forschungsberichte Künstliche Intelligenz''
		series includes primarily preliminary publications,
		specialized partial results, and supplementary
		material. In the interest of a subsequent final
		publication these reports should not be copied. All
		rights and the responsability for the contents of the
		report are with the authors, who would appreciate
		critical comments.

Gerhard Weiss