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This paper explores the benefits and challenges of using the ACT-R cognitive architecture in the development of a large-scale, functional, cognitively motivated language analysis model. The paper focuses on chunks, inheritance, production matching and memory, proposing extensions to ACT-R to support multiple inheritance and suggesting a mapping from the focus of attention, working memory and long-term memory to ACT-R buffers and declarative memory (DM).