Automatic knowledge acquisition is one of the bottlenecks in artificial intelligence and large-scale applications of natural language processing (NLP). There are many efforts to create large knowledge bases (KBs) or to automatically derive knowledge from large text corpora. On the one hand, we meet KBs like CYC, where a tremendous amount of work has been invested by knowledge enterers who have manually formalized large stocks of knowledge. The other extreme are projects using flat (mostly statistically based) methods for extracting knowledge from texts. These techniques seldom produce results with a clear semantic interpretation and sufficient quality for NLP applications, however. MAC-QUIK is a project to automatically acquire knowledge from natural language sources (like text corpora or lexicons) by means of a deep syntacticosemantic analysis and subsequent assimilation of the generated representations into a coherent KB. The paper emphasizes the role of a homogeneous formalism for interfacing between NLP and inferential question answering, and it demonstrates its use for a deductive treatment of coreference resolution
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