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CS4025: Grammars

CS4025: Grammars. Grammars for English Features Definite Clause Grammars See J&M Chapter 9 in 1 st ed, 12 in 2 nd , Prolog textbooks (for DCGs). Grammar: Definition. The surface structure (syntax) of sentences is usually described by some kind of grammar .

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CS4025: Grammars

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  1. CS4025: Grammars • Grammars for English • Features • Definite Clause Grammars See J&M Chapter 9 in 1st ed, 12 in 2nd, Prolog textbooks (for DCGs)

  2. Grammar: Definition The surface structure (syntax) of sentences is usually described by some kind of grammar. • A grammar defines syntactically legal sentences. • John ate an apple (syn legal) • John ate apple (not syn legal) • John ate a building (syn legal) • More importantly, a grammar provides a description of the structure of a legal sentence

  3. Facts about Grammars • Nobody has ever constructed a complete and correct grammar for any natural language • There is no single “correct” grammar for any natural language (though certain terminology has emerged that is generally useful) • Linguists even dispute exactly what formalism should be used for stating a grammar • Grammars describe subsets of NLs. They all have their strengths and weaknesses.

  4. Sentence Constituents • Grammar rules mention constituents (e.g. NP – noun phrase) as well as single word types (noun) • A constituent can occur in many rules • NP also after a preposition (near the dog), in possessives (the cat’s paws),... • These capture to some extent ideas such as: • Where NP occurs in a rule, any phrase of this class can appear • NP’s have something in common in terms of their meaning • Two phrases of the same kind, e.g. NP, can be joined together by the word “and” to yield a larger phrase of the same kind. Phrase structure tests.

  5. Common Constituents • Some common constituents • Noun phrase: the dog, an apple, the man I saw yesterday • Adjective phrase: happy, very happy • Verb group: eat, ate, am eating, have been eating. • Verb phrase: eating an apple, ran into the store • Prepositional phrase: in the car • Sentence: A woman ate an apple. • Constituents named “X phrase” in general have a most important word, the head, of category X (underlined above). The head determines properties of the rest of the phrase.

  6. Phrase-Structure Grammar • PS grammars use context-free rewrite rules to define constituents and sentences • S => NP Verb NP • Similar to rules used to define syntax of programming languages. • if-statement => if condition then statement • Also called context-free grammar (CFG) because the context does not restrict the application of the rules. • Adequate for (almost all of?) English • see J&M, chap 13

  7. Another simple grammar • S => NP VP • NP => DetAdj* Noun • NP => ProperNoun • VP => IntranVerb • VP => TranVerb NP • Det: a , the • Adj: big , black • Noun: dog , cat • ProperNoun: Fido , Misty, Sam • IntranVerb: runs, sleeps • TranVerb: chases, sees, saw How does this assign structure to simple sentences?

  8. Sentence VP (VerbPhrase) NP (NounPhrase) Example: Sam sleeps IntranVerb ProperNoun Rewrite/expansion rules. Sam sleeps

  9. Sentence VP NP Det NP Det Ex: The cat sees the dog TranVerb Noun Noun The cat sees the dog

  10. Sentence Det Fido chases the big black cat VP NP TranVerb ProperNoun NP Adj Adj Noun Fido chases the big black cat

  11. Recursion • NL Grammars allow recursion NP = Det Adj* CommonNoun PP* PP = Prep NP • For example: The man in the store saw John I fixed the intake of the engine of BA’s first jet.

  12. Sentence Det Det The man in the store saw John NP VP PP Noun Prep NP Noun The man in the store [saw John]

  13. Grammar is not everything • Sentences may be grammatically OK but not acceptable (acceptability?) • The sleepy table eats the green idea. • Fido chases the black big cat. • Sentences may be understandable even if grammatically flawed. • Fido chases a cat small. BUT: • Grammar terminology may be useful for describing sentences even if they are flawed (e.g. in tutoring systems) • Grammars may be useful for analysing fragments within unrestricted and badly-formed text.

  14. Features • Features are annotations on words and consituents • Grammar rules use unification (as in Prolog) to manipulate features. See references on unification, which is informally feature matching to the most common feature set. • Allow grammars to be smaller and simpler. If we accommodated all features in the above grammar, it would be very complex. • See J&M, chap 11 (1st edition), chap 15 (2nd edition)

  15. Ex: Number • One feature is [number:N], where N is singular or plural. • A noun’s number comes from morphology (and semantics) • cat - singular noun • cats - plural noun • An NP’s number comes from its head noun • A VP’s number comes from its verb (aux verb?) • is happy - singular VP • are happy - plural VP • NP and VP must agree in number: • The rat has an injury • *The rat have an injury

  16. Ex: Number • Similar issue between determiners and the head noun: • This tall man... • *This tall men... • *These tall man... • These tall men... • Languages can be richer (Slavic) or poorer (English, Chinese) morphologically. • Many different sorts of features in the world's languages.

  17. Modified Grammar • S => NP[num:N] VP[num:N] • NP[num:N] => Det[num:N] Adj* Noun[num:N] PP* • NP[num:singular] => ProperNoun • VP[num:N] => IntranVerb[num:N] • VP[num:N] => TranVerb[num:N] NP • PP => Prep NP • Noun[num:sing]: dog • Noun[num:plur]: dogs etc. • ??Sentence features?

  18. Example rule NP[num:N] = Det[num:N] Adj* Noun[num:N] • An NP’s determiner and noun (but not its adjectives in English v Russian) must agree in number. • this dog is OK • these dogs is OK • this dogs is not OK (det must agree) • these dog is not OK (det must agree) • the big red dog is OK • the big red dogs is OK (adjs don’t agree) • [number of NP] is [number of Noun] • Might also need some 'feature passing' in complex structures.

  19. Sentence[plur] Det[plural] Det[sing] Ex: These cats see this dog VP[plur] NP[plur] TranVerb[plur] NP[sing] Noun[plur] Noun[sing] These cats see this dog

  20. *Sentence[] Det[sing] Det[sing] Ex: *This cats see this dog VP[plur] NP[??] TranVerb[plur] NP[sing] Noun[plur] Noun[sing] This cats see this dog

  21. Other features • Person (I, you, him) • Verb form (past, present, base, past participle, pres participle) • Gender (masc, fem, neuter) • Polarity (positive, negative) • etc, etc, etc

  22. Definite Clause Grammars • A definite clause grammar (DCG) is a CFG with added feature information • The syntax is also Prolog-like, and DCGs are usually implemented in Prolog • You don’t need to know Prolog to use DCGs • The formalism was (partially) invented in Scotland

  23. DCG Notation Rule notationDCG notation S => NP VP s ---> np, vp. S[num:N] => NP[num:N] VP[num:N] s(Num) ---> np(Num), vp(Num). N => dog n ---> [dog]. N => New York n ---> [new, york]. NP => <name> np ---> [X], {isname(X)}. NB the number of “-” in the “--->” varies according to the implementation Variables have to match, e.g. N, Num, X....

  24. DCG Notation (2) • Use ---> between rule LHS and RHS. • Non-terminal symbols (e.g., np) written as Prolog constants (start with lower case letters) • Features (eg, Num) are arguments in () • Features are shown by position, not name • Prolog variables (start with upper case letters) indicate unknown values • Actual words are lists, in [] • Prolog tests can be attached in {} • (Optional) use distinguished predicate to define the goal symbol (eg, s).

  25. An example DCG distinguished(s). s ---> np(Num), vp(Num). np(Num) ---> det(Num), noun(Num). np(sing) ---> name. vp(Num) ---> intranverb(Num). vp(Num) ---> tranverb(Num), np(_). det(_) ---> [the]. noun(sing) ---> [dog]. name ---> [sam]. intranverb(sing) ---> [sleeps]. intranverb(plur) ---> [sleep]. tranverb(sing) ---> [chases]. tranverb(plur) ---> [chase].

  26. DCG’s and Prolog • DCG’s are translated into Prolog in a fairly straightforward fashion • See Shapiro, The Art of Prolog • In fact, Prolog was originally designed as a language for NLP, as part of a machine-translation project. • Prolog has evolved since, but is still well-suited to many NLP operations.

  27. Edinburgh Package • We will use a parsing system for DCGs developed (for teaching purposes) by Holt and Ritchie from Edinburgh University. • Use SWI Prolog

  28. Using the package • Statements always end with “.” • Prompt is ?- • Type code into a file, not directly into the interpreter. • Load files with [file]. • .pl = prolog source • By default, type “n.” to backtrack request

  29. Using the DCG package ?- prp([sam, chases, the, dog]). parse and pretty print ?- prp(np, [the, dog]). parse and print an np ?- parse([sam, chases, the, dog], X). returns parse in X ?- parse(np, [the, dog], X). parse a constituent (np) For all the above, after each parse is printed, you will be asked if you want to look for additional parses NB for other DCG implementations, the grammar writer explicitly builds parse structures in the grammar rules.

  30. Other Grammar Formalisms • Many other grammar formalisms • GPSG, HPSG, LFG, TAG, ATN, GB,... • No consensus on which is best, although many strong opinions (like C vs Pascal vs Lisp) • Like programming languages, formalism used often determined by experience of developers, and available support tools

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