Natural Language Processing AI - Weeks 19 Natural Language Processing PART 2 Lee McCluskey, room 2/07

Natural Language Processing NLP: the process Natural Language Generation Translation Summary/ Classification Knowledge Base: representation of meaning UNDERSTANDING PROCESS (“Natural Language Understanding”) Text (sentence, , news story..) -- Parsing -- Referencing -- Meaning Extraction and Integration

Natural Language Processing NLP: the process Knowledge Base: representation of meaning UNDERSTANDING PROCESS (“Natural Language Understanding”) Text “The cat sits on the mat” sentence npvp The cat sits on the mat Fact(type: statement, agent: cat-002, action: sits_on, object: mat-001) Fact(type: statement, agent: Fido, action: is_a, object: cat) Fact(type: statement, agent: Freda, action: loves, object: Fido)

Natural Language Processing NLP (NLU) : typical process 1.Scanning 2.Parsing 3.Finding referents for pronouns etc 4.Resolving Ambiguities 5.Meaning Extraction 6.Meaning Integration

Natural Language Processing NLP (NLU) : tools to help Parser- Generators (e.g. YACC, JavaCUP) are tools which input BNF Grammars and output Parsers for computer languages such as programming languages. Extra information can be added to the Grammar to make the Parser output useful information. PGs can be used for Natural Language Processing also. Parser Generator Grammar Parser Sentence / program Parse Tree or Syntax Error Report

Natural Language Processing NLP (NLU) : Definite Clause Grammars Prolog has a built in Parser Generator tool called the “Definite clause grammar” See This interprets grammar rules as “recursive descent” parsers (hence generating a parser immediately from the grammar). DCG - very simple example – here is a BNF grammar: --> --> i | sentences --> parse

Natural Language Processing NLP (NLU) : Definite Clause Grammars Grammar with this Prolog-looking syntax sentence --> noun, verb, noun. noun --> [i]. noun --> [sentences]. verb --> [parse]. Input this to the Prolog Interpreter gives a listing of: sentence(A, B) :- noun(A, C), verb(C, D), noun(D, B). noun(A, B) :- A = [ i | B]. noun(A, B) :- A = [sentences | B]. verb(A, B) :- A = [ parse | B ]. Try it out with sentence([i,parse,sentences],A).

Natural Language Processing Larger Example – BNF grammar for a bit of English sentence --> noun_phrase verb_phrase noun_phrase--> determiner adjective noun noun_phrase--> adjective noun noun_phrase--> determiner noun noun_phrase --> noun verb_phrase--> verb noun_phrase verb_phrase--> verb preposition noun_phrase determiner --> a | an adjective --> fruit noun --> flies | fruit | time | arrow noun --> banana verb --> like | flies preposition --> like

Natural Language Processing Example in Prolog DCG syntax sentence --> noun_phrase, verb_phrase. noun_phrase--> determiner, adjective, noun. noun_phrase--> adjective, noun. noun_phrase--> determiner, noun. verb_phrase--> verb, noun_phrase. verb_phrase--> verb, preposition, noun_phrase. determiner --> [a]. adjective --> [fruit]. noun --> [flies]. noun --> [banana]. noun --> [fruit]. noun --> [time]. noun --> [arrow]. verb --> [like]. verb --> [flies]. preposition --> [like]. This Grammar can now be loaded into Prolog and ACTS LIKE A PARSER.

Natural Language Processing Prolog Grammar Rules sentence(A, B) :- noun_phrase(A, C), verb_phrase(C, B). noun_phrase(A, B) :- determiner(A, C), adjective(C, D), noun(D, B). noun_phrase(A, B) :- adjective(A, C), noun(C, B). noun_phrase(A, B) :- determiner(A, C), noun(C, B). verb_phrase(A, B) :- verb(A, C), noun_phrase(C, B). verb_phrase(A, B) :- verb(A, C), preposition(C, D), noun_phrase(D, B). determiner(A,B) :- A = [a|B]. adjective(A,B) :- A = [fruit| B]. noun(A,B) :- A = [flies|B]. noun(A,B) :-A = [banana|B]. noun(A,B) :-A= [fruit|B]. noun(A,B) :-A = [time|B]. noun(A,B) :-A = [arrow|B]. verb(A,B) :-A = [like|B]. verb(A,B) :-A = [flies|B]. preposition(A,B) :- A = [like|B]. This is the Prolog Code that the Grammar Translates to. The variables implement a kind of “stream processing”

Natural Language Processing Getting More From the Parsing Stage Other functions we might need – extract information about the parse, and ensure constraints are maintained in various parts of the sentence, eg - Return parse tree - Check consistency of gender - Checking consistency of number sentence(sentence( NP, VP)) --> noun_phrase(NP, No, Gender), verb_phrase(VP, No, Gender). -- build up parse tree as “self describing” term -- when a part of the sentence commits to number or gender, then record it for consistency

Natural Language Processing NLP (NLU) : Definite Clause Grammars Grammar with this Prolog-looking syntax sentence(s(subject(S),V,object(O))) --> noun(S), verb(V), noun(O). noun(noun(i)) --> [i]. noun(noun(sentence)) --> [sentences]. verb(verb(parse)) --> [parse]. Input this to the Prolog Interpreter gives a listing of: sentence(s(subject(S), V, object(O)), A, B) :- noun(S, A, C), verb(V, C, D), noun(O, D, B). noun(noun(i), A, B) :- A = [ i | B]. noun(noun(sentence), A, B) :- A = [sentences | B]. verb(verb(parse), A, B) :- A = [ parse | B ]. Try it out sentence(Tree, [i,parser,sentences],X). X = [], Tree = s(subject(noun(i)),verb(parser),object(noun(sentence))) ? yes

Natural Language Processing Parsing + Returning a Parse Tree /* syntax bit + No (-singular or plural) + Gender (-Masc, Fem or Inamin.)*/ s entence(sentence( NP, VP)) --> noun_phrase(NP, No, Gender), verb_phrase(VP, No, Gender). noun_phrase(noun_phrase([D,N]), No,Gender) --> d(D, No,Gender), n(N, No,Gender). verb_phrase(verb_phrase([V,N]), No,Gender) --> v(V, No,Gender), noun_phrase(N, _,_). /* no ref to no or gen of object*/ verb_phrase(verb_phrase([V]), No,Gender) --> v(V, No,Gender). d(determiner(the), No,Gender) --> [the]. d(determiner(a), singular,Gender) --> [a]. d(determiner(some), plural,Gender) --> [some]. d(determiner(all), plural,Gender) --> [all]. n(noun(apple), singular, inanimate) --> [apple]. n(noun(apples), plural, inanimate) --> [apples]. n(noun(man), singular, animate) --> [man]. n(noun(men), plural, animate) --> [men]. v(verb(eat), singular, animate) --> [eats]. v(verb(eats), plural, animate) --> [eat]. v(verb(sing), singular, animate) --> [sings]. v(verb(sings), plural, animate) --> [sing]. sentence(X,[the,man,eats,the,apples],Y). X = sentence( noun_phrase([determiner(the),noun(man)]), verb_phrase([verb(eat), noun_phrase([determiner(the),noun(apples)])])), Y = [] ?