ABSTRACT
Artificial intelligence (AI) is, or should be, at the heart of natural language processing (NLP) research. Practical natural language interfaces, writing aids, and machine translation systems all exist. But the public has not been quick to accept what we can produce. With all due respect, the rate of progress in NLP has been disappointing to many. When NLP research first began, linguists were preoccupied with syntax, so AI researchers had no choice but to cobble together semantic theories as best they could. NLP is desperate for good methods whereby contextual constraints can be brought to bear in a timely fashion to help resolve AI problems as lexical and structural ambiguity in language analysis. AI's unique contribution to the study of the mind stems from its dedication to the proposition that functional considerations arising from the need to perform realistic tasks, rather than considerations of parsimonious empirical description, should be the primary constraints on cognitive theories.
