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SYNTACTIC ANALYSIS
Context-Free Grammars, Grammar rules for English, Treebanks, Normal Forms for grammar – Dependency Grammar – Syntactic Parsing, Ambiguity, Dynamic Programming parsing – Shallow parsing – Probabilistic CFG, Probabilistic CYK, Probabilistic Lexicalized CFGs - Feature structures, Unification of feature structures. Syntactic analysis, also known as parsing, is the process of analysing the structure of sentences according to a given grammar. Here’s a breakdown of the key topics mentioned:
1. Context-Free Grammars (CFG) A Context-Free Grammar (CFG) is a formal grammar that consists of a set of rules used to generate sentences in a language. It includes non-terminal symbols, terminal symbols, a start symbol, and production rules. Example: Mathematica S → NP VP NP → Det N | N VP → V NP | V
Det → "the" | "a" N → "dog" | "cat" V → "chased" | "saw" This grammar can generate sentences like "the dog chased the cat."
2. Grammar Rules for English English grammar rules define how words and phrases combine to form valid sentences. Common syntactic structures include: o Noun Phrase (NP) : Determiner + Noun ( the cat ) o Verb Phrase (VP) : Verb + Object ( chased the cat ) o Prepositional Phrase (PP) : Preposition + NP ( on the table ) 3. Treebanks A Treebank is a dataset containing sentences annotated with syntactic structures (parse trees). Example: The Penn Treebank is widely used for training parsers.
Greibach Normal Form (GNF) : Every production starts with a terminal followed by non-terminals.
5. Dependency Grammar Focuses on word-to-word relationships rather than phrase structure. Example: "The dog chased the cat." o "chased" → governs "dog" (subject) and "cat" (object). Used in dependency parsing to determine syntactic roles.
6. Syntactic Parsing Constituency Parsing : Uses CFG to build a tree representing sentence structure. Dependency Parsing : Builds a tree showing relationships between words.
8. Dynamic Programming Parsing CYK Algorithm (Cocke-Younger-Kasami): Parses sentences efficiently using CNF. Earley Parser : Handles any CFG efficiently, even left-recursive grammars. 9. Shallow Parsing Also called chunking , extracts only major phrases (NP, VP) without full parsing. Example: o Sentence: "The quick brown fox jumps over the lazy dog." o Chunking output: [NP The quick brown fox] [VP jumps over] [NP the lazy dog] 10. Probabilistic CFG (PCFG) CFG with probabilities assigned to rules. Helps in selecting the most likely parse. Example: VP → V NP [0.7] VP → V [0.3] 11. Probabilistic CYK Parsing CYK algorithm combined with PCFG to find the most probable parse. 12. Probabilistic Lexicalized CFGs
Extends PCFG by considering lexical information (specific words rather than generic categories).
13. Feature Structures Represent additional grammatical information like gender, number, tense. Example: yaml [Noun: "dog", Number: singular, Gender: neutral] 14. Unification of Feature Structures Combines feature structures to ensure grammatical consistency. Used in unification-based parsing like Lexical Functional Grammar (LFG).
