Relationship between Natural Language Processing and AI: Role of Constrained Formal‐Computational Systems

Modeling various aspects of language—syntax, semantics, pragmatics, and discourse, among others—by the use of constrained formal‐computational systems, just adequate for such modeling, has proved to be an effective research strategy, leading to deep understanding of these aspects, with implications for both machine processing and human processing. This approach enables one to distinguish between the universal and stipulative constraints. This is in contrast to an approach where we start with the most powerful formal‐computational system and then model the phenomena by making all constraints stipulative in a sense. The use of constrained systems for modeling leads to some novel ways of describing locality of structures and brings out the relationship between the complexity of description of primitives and local computations over them. These ideas serve to unify theoretical, computational, and statistical aspects of natural language processing in AI. It is expected that this approach will also be productive in other domains of AI.