TY  - THES
T1  - Linguistic-based computational treatment of textual entailment recognition
A1  - Amoia,Marilisa
Y1  - 2009/01/08
N2  - In this thesis, I investigate how lexical resources based on the organisation of lexical knowledge in classes which share common (syntactic, semantic, etc.) features support natural language processing and in particular symbolic recognition of textual entailment.  First, I present a robust and wide coverage approach to lexico-structural verb paraphrase  recognition based on Levin's (1993) classification of English verbs. Then, I show that by  extending Levin's framework to general inference patterns, a classification of English adjectives  can be obtained that compared with previous approaches, provides a more fine  grained semantic characterisation of their inferential properties. Further, I develop a  compositional semantic framework to assign a semantic representation to adjectives based on  an ontologically promiscuous approach (Hobbs, 1985) and thereby  supporting first order inference for all types of adjectives including extensional ones.  Finally, I present a test suite for adjectival inference I developed as a resource for  the evaluation of computational systems handling natural language inference.
KW  - Semasiologie
KW  - Sprachverarbeitung
KW  - Entailment
CY  - Saarbrücken
PB  - Universitäts- und Landesbibliothek
AD  - Postfach 151141, 66041 Saarbrücken
UR  - http://scidok.sulb.uni-saarland.de/volltexte/2009/2036
ER  -