This research aims to develop a conceptual natural language processing (NLP) system with adaptable components that can be easily tailored for different domains and applications. The architecture of the system consists of fine-grained layers to support various depths of text processing. The shallow layers support syntactic processing, which may be sufficient for some information retrieval tasks, while the deeper layers support semantic and conceptual processing for in-depth language understanding. The system includes components for part-of-speech tagging, prepositional phrase attachment, semantic feature identification, and concept extraction. Each component can be tailored for new domains with minimal manual effort. The layered architecture also allows students to develop individual components and plug them in to the larger system for experimentation. The education goals are to use the system as the basis for a hands-on science workshop for young girls, for summer lectures to high school students, for class projects in natural language processing and machine learning, and for graduate and undergraduate research projects. The purpose of the research is to develop techniques for building conceptual natural language understanding systems automatically or semi-automatically for new domains. Generating conceptual sentence analyzers quickly and efficiently is an important step toward many practical applications, including conceptual information retrieval, text categorization, and information extraction.
