Feature logic-based semantic composition : a comparison between LRS and LTAG

In this paper we will explore the similarities and differences between two feature logic-based approaches to the composition of semantic representations. The first approach is formulated for Lexicalized Tree Adjoining Gr
In this paper we will explore the similarities and differences between two feature logic-based approaches to the composition of semantic representations. The first approach is formulated for Lexicalized Tree Adjoining Grammar (LTAG, Joshi and Schabes 1997), the second is Lexical Ressource Semantics (LRS, Richter and Sailer 2004) and was first defined in Head-driven Phrase Structure Grammar. The two frameworks have several common characteristics that make them easy to compare: 1 They use languages of two sorted type theory for semantic representations. 2. They allow underspecification. LTAG uses scope constraints while LRS provides component-of contraints. 3 They use feature logics for computing semantic representations. 4. they are designed for computational applications. By comparing the two frameworks we will also point outsome characteristics and advantages of feature logic-based semantic computation in genereal.
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Metadaten
Author:Laura Kallmeyer, Frank Richter
URN:urn:nbn:de:hebis:30-1110302
Document Type:Article
Language:English
Date of Publication (online):2008/10/20
Year of first Publication:2007
Publishing Institution:Univ.-Bibliothek Frankfurt am Main
Release Date:2008/10/20
Tag:Lexical Ressource Semantics; Lexicalized Tree Adjoining Grammar
Source:http://www.sfb441.uni-tuebingen.de/~lk/papers/richterkallmeyer.pdf ; Postproceedings of the 1st International Workshop on Typed Feature Structure Grammars (TFSG06), hrsg. v. Anders Søgaard / Petter Haugereid, S. 31-83.
HeBIS PPN:206693958
Dewey Decimal Classification:400 Sprache
Sammlungen:Linguistik
Linguistic-Classification:Linguistik-Klassifikation: Computerlinguistik / Computational linguistics
Licence (German):License Logo Veröffentlichungsvertrag für Publikationen

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