By Kyle McCardle

 Issues with Natural Language  Basic Components  Syntax  The Earley Parser  Transition Network Parsers  Augmented Transition Networks  Stochastic Tools  Applications

 A large amount of human knowledge is assumed  Language is pattern based ◦ Syllables -> words -> phrases -> sentences  Language occurs in a complex environment

 Language lexicon ◦ Complete catalog of recognized words  Parser and set of grammar rules ◦ Pulls apart sentences for internal representation  Semantic theory ◦ Tool for deriving meaning from internal representation

 Terminals  Nonterminals  Top-down derivation  Parse tree

 Dynamic Programming ◦ Memoization  Dotted grammar rules ◦ Predict, Scanned, Completed Sentence → ∙ Noun Verb Noun → ∙ john Noun → john ∙ Sentence → Noun ∙ Verb

 Addresses issue of semantic relationships  Terminal and nonterminal arcs  Context-sensitive grammars

 Extend transition networks by utilizing stored procedures associated with arcs  Procedures assign grammatical qualities to a given word (part of speech, root, number)  Case frames provide semantic context

 View language as a random process ◦ p(t 1, …, t n | w 1, …, w n )  Markov model approach  Decision tree approach  Probabilistic approaches

 Reading and comprehension  Translators  Relational database front end  Information extraction from the web

Earley parser. (n.d.). Retrieved from ki100k/docs/Earley_parser.html Luger, G. (2009). Artificial intelligence: Structures and strategies for complex problem solving. (6th ed.). Boston: Pearson Education, Inc. Stochastic semantic analysis. (n.d.). Retrieved from ntic_analysis Zhaoyin, Z. (2009). Rule-based natural language understanding based on fuzzy evaluation of teaching quality International Forum on Computer Science-Technology and Applications