Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 3 of 3
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.
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.
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.