Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 7 of 7
Full-Text Articles in Syntax
Learning In Minimalism-Based Language Modeling, Deryle W. Lonsdale
Learning In Minimalism-Based Language Modeling, Deryle W. Lonsdale
Faculty Publications
The natural language version of the Soar cognitive modeling system (Newell, 1990) has enabled a number of language modeling applications from on-line parsing behavior (Lewis, 1993) to simultaneous interpretation (Lonsdale, 1997, 1998) to robotic control (Benjamin, Lonsdale, & Lyons, 2004). The system supports an integrated approach to incremental comprehension and generation. Learning mechanisms account for processes in language performance from deliberate, explicit reasoning to automatic, recognitional expertise.
Syntactic processing in prior versions of the system followed the Principles and Parameters approach to syntax.
Xnl-Soar, Incremental Parsing, And The Minimalist Program, Deryle W. Lonsdale, Lareina Hingson, Jamison Cooper-Leavitt, David W. Casbeer, Rebecca Madsen
Xnl-Soar, Incremental Parsing, And The Minimalist Program, Deryle W. Lonsdale, Lareina Hingson, Jamison Cooper-Leavitt, David W. Casbeer, Rebecca Madsen
Faculty Publications
Minimalist Principles (Chomsky 1995)
Hierarchy of Projections (Adger 2003)
Features play a central role
NP, VP symmetry including shells
Learning In Minimalism-Based Language Modeling, Deryle W. Lonsdale
Learning In Minimalism-Based Language Modeling, Deryle W. Lonsdale
Faculty Publications
The natural language version of the Soar cognitive modeling system (Newell, 1990) has enabled a number of language modeling applications from on-line parsing behavior (Lewis, 1993) to simultaneous interpretation (Lonsdale, 1997, 1998) to robotic control (Benjamin, Lonsdale, & Lyons, 2004). The system supports an integrated approach to incremental comprehension and generation. Learning mechanisms account for processes in language performance from deliberate, explicit reasoning to automatic, recognitional expertise.
Syntactic processing in prior versions of the system followed the Principles and Parameters approach to syntax.
Expanding Tree Adjoining Grammar To Create Junction Grammar Trees, Deryle W. Lonsdale, Ronald Millett
Expanding Tree Adjoining Grammar To Create Junction Grammar Trees, Deryle W. Lonsdale, Ronald Millett
Faculty Publications
Junction Grammar (JG) combines junction operators, multiple linked syntax/semantics trees, and flexible traversal algorithms. The multiple tree and flexible ordering characteristics of MC-TAG and other TAG extensions are somewhat analogous. This paper proposes that these similarities can be integrated to form a new approach, JG-TAG. Relevant aspects of both theories and the proposed new model are discussed in turn, and representative examples are sketched.
Nl-Soar Update, Deryle W. Lonsdale
A Categorial Grammar Fragment For Lushootseed, Deryle W. Lonsdale
A Categorial Grammar Fragment For Lushootseed, Deryle W. Lonsdale
Faculty Publications
This paper establishes a basic framework for a Categorial Grammar (CG) description ofLushootseed morpho syntax and semantics. After a brief review of English CG principles, a preliminary attempt is made to apply these fundamentals to Lushootseed free and bound morphemes. Several sample derivations are presented. including one rather complex syntactic ambigiuty previously noted in the literature with its alternative descriptions. Once the syntactic elements have been put in place a short overview is presented of how semantic interpretation can be established in tandem with syntactic composition principles. Finally, the implementation of this framework within the Attribute Logic Engine environment is …
An Operator-Based Account Of Semantic Processing, Deryle W. Lonsdale, C. Anton Rytting
An Operator-Based Account Of Semantic Processing, Deryle W. Lonsdale, C. Anton Rytting
Faculty Publications
This paper explores issues of psychological plausibility in modeling natural language understanding within Soar, a symbolic cognitive model. It focuses on constructing syntactic and semantic representations in simulated real time, with particular emphasis on word sense disambiguation (WSD). We discuss (i) what level of WSD should be modeled and (ii) how to use resources such as WordNet to inform these models. A preliminary model of coarse-grained WSD is included to show how syntactic, semantic, and other knowledge sources interact in Soar. Finally, we explore issues of interleaving, learning, and integrating other WSD approaches with Soar's native model of learning.