Using Case-Based Reasoning in Natural Language Processing.

摘要

A variety of problems addressed in natural language processing (NLP) make this area ripe for hybrid system designs and approaches based on multiple technologies. One particularly promising crossover is the application of case-based reasoning (CBR)to NLP. There are two important facts that make CBR an obvious candidate for innovative investigations in NLP. First, most decision processes in NLP are characterized by shades of grey and different ways of weighting preferences rather than black and white absolutes or right and wrong answers. This holds true for the lowest levels of lexical ambiguity as well as the highest levels of inference and reasoning. The affinity in language for relative preference rather than hard and fast absolutes is fully consistent with CBR capabilities that can produce multiple solutions and then assess each one in accordance with multiple dimensions for evaluation.