neural networks research group
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DISCERN
DISCERN is a large, modular neural network system for reading, paraphrasing and answering questions about stereotypical (script-based) stories. A precis of this book and a short summary paper should give you a quick overview of this research. To get an idea what the DISCERN programs are like (without having to first port them), take a look at the on-line demo. It runs remotely on cascais.cs.utexas.edu, with graphics display on your X11 screen. The DISCERN software consists of four components: 1. PROC for training and testing the backpropagation-based processing modules (for parsing, generation, and question answering), 2. HFM for training and testing the hierarchical feature maps that form the basis for the episodic memory, 3. DISLEX for training and testing the lexicon (lexical and semantic feature maps and associative connections between them), and 4. DISCERN, which is the integrated complete story processing model put together from the final results of the above three programs. Comments to discern@cs.utexas.edu.
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TAR
People
Risto Miikkulainen
Publications
A Subsymbolic Model of Language Pathology in Schizophrenia
(2007)
Text and Discourse Understanding: The DISCERN System
(2002)
Script-Based Inference And Memory Retrieval In Subsymbolic Story Processing
(1995)
Integrated Connectionist Models: Building AI Systems on Subsymbolic Foundations
(1994)
Subsymbolic Natural Language Processing: An Integrated Model Of Scripts, Lexicon, And Memory
(1993)
DISCERN: A Distributed Artificial Neural Network Model Of Script Processing And Memory
(1990)
Projects
Processing Script-Based Stories: The DISCERN System
Storing Information on Maps: The Trace Feature Map Model
Neural Network Models of Schizophrenic Language
Areas of Interest
Natural Language Processing
Cognitive Science
Episodic Memory
Demos
A Subsymbolic Model of Schizophrenic Language