Not for Its Own Sake: Knowledge as Byproduct of NLP

William Dolan and Stephen Richardson

Recent claims in the literature as well as current research trends suggest that a long-held goal of natural language processing, the ability to map automatically from machine readable dictionaries into structured knowledge bases that can be used for various artificial intelligence tasks may be impossible. This paper argues to the contrary, describing an extremely large and rich lexical knowledge base which has been created almost as a byproduct of ordinary morphological, syntactic and semantic processing within a broad-coverage NLP system. This approach to mapping between text and knowledge representation appears to offer the only viable hope for creating knowledge bases large and rich enough to support complex tasks like machine understanding and domain-independent reasoning.


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