1. By Kyle McCardle

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

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

4.  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

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

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

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

8.  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

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

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

12. Earley parser. (n.d.). Retrieved from http://www.princeton.edu/~achaney/tmve/wi ki100k/docs/Earley_parser.html http://www.princeton.edu/~achaney/tmve/wi 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 http://en.wikipedia.org/wiki/Stochastic_sema ntic_analysis http://en.wikipedia.org/wiki/Stochastic_sema Zhaoyin, Z. (2009). Rule-based natural language understanding based on fuzzy evaluation of teaching quality. 2009 International Forum on Computer Science-Technology and Applications