Text and Discourse Understanding: The DISCERN System
Risto P Miikkulainen · CogPrints (University of Southampton) · 2002
Introduction The subsymbolic approach to natural language processing (NLP) captures a number of intriguing properties of human-like information processing such as learning from examples, context sensitivity, generalization, robustness of behavior, and intuitive reasoning. Within this new paradigm, the central issues are quite different from (even incompatible with) the traditional issues in symbolic NLP, and the research has proceeded without much in common with the past. However, the ultimate goal is still the same: to understand how humans process language. Even if NLP is being built on a new foundation, as can be argued, many of the results obtained through symbolic research are still valid, and could be used as a guide for developing subsymbolic models of natural language processing. This is where DISCERN (DIstributed SCript processing and Episodic memoRy Network [18]), a subsymbolic neural network model of script-based story understanding, fits in. DISCERN is purely a sub