'Grammar g' presentation slideshows

Part III Hierarchical Bayesian Models

Part III Hierarchical Bayesian Models. Universal Grammar. Hierarchical phrase structure grammars (e.g., CFG, HPSG, TAG). Grammar. Phrase structure. Utterance. Speech signal. Vision. (Han and Zhu, 2006). Word learning. Whole-object principle Shape bias Taxonomic principle

1.39k views • 114 slides

Constructing Grammar: a computational model of the acquisition of early constructions

Constructing Grammar: a computational model of the acquisition of early constructions. CS 182 Lecture April 25, 2006. What constitutes learning a language?. What are the sounds (Phonology) How to make words (Morphology) What do words mean (Semantics)

594 views • 42 slides

Chapter 4: Top-Down Parsing

Chapter 4: Top-Down Parsing . Objectives of Top-Down Parsing. an attempt to find a leftmost derivation for an input string. an attempt to construct a parse tree for the input string starting from the root and creating the nodes of the parse tree in preorder . . Input String :. lm. lm. lm.

486 views • 34 slides

Chapter 4 Syntax Analysis

Chapter 4 Syntax Analysis. Topics to cover: Context-Free Grammars: Concepts and Notation Writing and rewriting a grammar Syntax Error Handling and Recovery. Introduction. Why CFG CFG gives a precise syntactic specification of a programming language.

662 views • 28 slides

Lecture # 8

397 views • 23 slides

Properties of LL(1)

Properties of LL(1). No ambiguous or left recursive grammar can be LL(1). Grammar G is LL(1) iff whenever A ? ? | ? are two distinct productions of G and: For no terminal a do both ? and ? derive strings beginning with a . FIRST( ? )?FIRST(?)=?

119 views • 3 slides