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Computing Science, University of Aberdeen 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)
Computing Science, University of Aberdeen 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
Computing Science, University of Aberdeen 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.
Computing Science, University of Aberdeen 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.
Computing Science, University of Aberdeen 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.
Computing Science, University of Aberdeen 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
Computing Science, University of Aberdeen 7
Another simple grammar
– S => NP VP– NP => Det Adj* 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?
Computing Science, University of Aberdeen 8
Example: Sam sleeps
VP (VerbPhrase)
IntranVerb
Sam sleeps
Sentence
ProperNoun
NP (NounPhrase)
Rewrite/expansion rules.
Computing Science, University of Aberdeen 9
Ex: The cat sees the dog
VP
Det Noun
TranVerb
cat sees the dog
Sentence
NP
The
Det Noun
NP
Computing Science, University of Aberdeen 10
Fido chases the big black cat
VP
Det Adj
TranVerb
Fido chases the big
Sentence
NPProperNoun
NP
Adj
black
Noun
cat
Computing Science, University of Aberdeen 11
Recursion
NL Grammars allow recursionNP = Det Adj* CommonNoun PP*PP = Prep NP
For example:The man in the store saw JohnI fixed the intake of the engine of BA’s first
jet.
Computing Science, University of Aberdeen 12
The man in the store saw John
man
Sentence
PP
Det Noun
Prep
in the store
NP
The
Det Noun
NP
VP
[saw John]
Computing Science, University of Aberdeen 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.
Computing Science, University of Aberdeen 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)
Computing Science, University of Aberdeen 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
Computing Science, University of Aberdeen 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.
Computing Science, University of Aberdeen 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?
Computing Science, University of Aberdeen 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.
Computing Science, University of Aberdeen 19
Ex: These cats see this dog
VP[plur]
Det[sing] Noun[sing]
TranVerb[plur]
cats see this dog
Sentence[plur]
NP[sing]
These
Det[plural] Noun[plur]
NP[plur]
Computing Science, University of Aberdeen 20
Ex: *This cats see this dog
VP[plur]
Det[sing] Noun[sing]
TranVerb[plur]
cats see this dog
*Sentence[]
NP[sing]
This
Det[sing] Noun[plur]
NP[??]
Computing Science, University of Aberdeen 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
Computing Science, University of Aberdeen 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
Computing Science, University of Aberdeen 23
DCG Notation
Rule notation DCG notationS => 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....
Computing Science, University of Aberdeen 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).
Computing Science, University of Aberdeen 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].
Computing Science, University of Aberdeen 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.
Computing Science, University of Aberdeen 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
Computing Science, University of Aberdeen 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
Computing Science, University of Aberdeen 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.
Computing Science, University of Aberdeen 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