Natural Language

Statistical Language Learning


New, exacting empirical methods are needed to break the deadlock in such areas of artificial intelligence as robotics, knowledge representation, machine learning, machine translation, and natural language processing (NLP). It is time, Charniak observes, to switch paradigms. This text introduces statistical language processing techniques -- word tagging, parsing with probabilistic context free grammars, grammar induction, syntactic disambiguation, semantic word classes, word-sense disambiguation -- along with the underlying mathematics and chapter exercises. Charniak points out that as a method of attacking NLP problems, the statistical approach has several advantages. It is grounded in real text and therefore promises to produce usable results, and it offers an obvious way to approach learning: "one simply gathers statistics."

Can logic programming execute as fast as imperative programming?


The output is assembly code for the Berkeley Abstract Machine (BAM). Directives hold starting from the next predicate that is input. Clauses do not have to be contiguous in the input stream, however, the whole stream is read before compilation starts. This manual is organized into ten sections.

Natural language interfaces


In Howard Shrobe, editor, Exploring Artificial Intelligence. Morgan Kaufmann