Semantic Rules & Machine Learning

Dr. Walid Saba discusses the limitations of the data-driven, statistical and machine learning (ML) approaches that are the currently dominant paradigm in the use of natural language processing (NLP) in text analytics. Using very simple examples, he argues that these methods can produce results that are, at best, Probably, Approximately, Correct. Moreover, these methods are not scalable as they require continuous training on massive amounts of data that are often not available. Instead, he argues for a semantic counter-revolution where deep semantic analysis as well as ontological knowledge repositories are employed. Link

This entry was posted in Knowledge Representation, Logic and AI, Machine Learning, Natural Language Processing. Bookmark the permalink.

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