Proceedings of the Annual Meeting of the Cognitive Science Society

Successful and robust natural language processing must efficiently integrate multiple types of information to produce an interpretation of input. Previous approaches often rely heavily on either syntax or semantics, verbspecific or highly general representations. A careful task analysis identifies principled subsets of information from across these spectra are needed. This presents challenges to efficient and accurate processing. W e present a modular architecture whose components reflect the distinct types of information used in processing. Its control mechanism specifies the principled manner in which components share information. W e believe this architecture provides benefits for processing sentences with novel verbs, ambiguous sentences, and sentences with constituents placed outside their canonical position.