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Using Definite Knowledge: NLP and nl_interface.pl

Using Definite Knowledge: NLP and nl_interface.pl. Notes for Ch.3 of Poole et al. CSCE 580 Marco Valtorta. Natural Language Processing (NLP) with Definite Clause Logic. nl_interface.pl Computational Linguistics is a big field Three reasons to study NLP

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Using Definite Knowledge: NLP and nl_interface.pl

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  1. Using Definite Knowledge:NLP and nl_interface.pl Notes for Ch.3 of Poole et al. CSCE 580 Marco Valtorta

  2. Natural Language Processing (NLP) with Definite Clause Logic • nl_interface.pl • Computational Linguistics is a big field • Three reasons to study NLP • Ease of communication with people (infobot) • Lots of information stored in NL • Many problems in AI arise in NL

  3. Syntax, Semantics, Pragmatics • Imagine finding these sentences at the beginning of the textbook: • This book is about computational intelligence. • Green frogs sleep soundly. • Colorless green ideas sleep furiously. • Noam Chomsky • Time flies like green bananas. • Robert Oakman • Sleep ideas green furiously colorless.

  4. Rules and Lists for NL • A context-free production rule: • sentence -> noun_phrase, verb_phrase • A corresponding definite clause: • sentence(S) <- noun_phrase(N), verb_phrase(V), append(N,V,S). • Sentences and phrases are represented as lists, e.g.: • Noun_phrase([computer]).

  5. Definite Clause Grammars • We may do away with explicit appendsby using difference lists. • This leads to definite clause grammars. • noun-phrase(T1,T2) is true if T1 – T2 is a noun phrase. E.g., • noun_phrase([the, computer, runs], [runs]). is true in the normal interpretation, since “the computer” is a noun phrase.

  6. Examples • sentence  noun_phrase, verb_phrase sentence(T0,T2)  noun-phrase(T0,T1) & verb_phrase(T1,T2). Read: “There is a sentence between T0 and T2 if there is a noun phrase between T0 and T1 and there is a noun phrase between T1 and T2.” • This reading is consistent with the view of the second part of a difference list as a pointer to the end of the list.

  7. More Examples • h  a,b,[c,d],e,[f],g • a, b, e, and g are nonterminal symbols: they get rewritten • c, d, and g are terminal symbols. • h(T0,T6)  a(T0,T6) & b(T1, [c,d|T3]) & e(T3,[f|T5]) & g(T5,T6).

  8. More Examples • noun-phrase(T0,T4)  det(T0,T1) & modifiers(T1,T2) & noun(T2,T3) & pp(T3,T4). • “A noun phrase is a determiner followed by modifiers followed by a noun followed by a prepositional phrase.” • det(T,T). Empty list: determiner is optional! det([a|T],T). det([the|T],T).

  9. Building Parse Trees • Use extra arguments. • This is done with recursive descent parsers in CSCE 531, where the atoms correspond to Java methods. • sentence(T0,T2,s(NP,VP))  noun_phrase(T0,T1,NP) & verb_phrase(T1,T2,VP). • Parse trees do not adequately represent the deep structure of a sentence.

  10. Enforcing Constraints • Parameters added to nonterminals enforce constraints • cf. attribute grammars; these are contextual grammars! • nl_numbera.pl is a grammar that enforces subject-verb number agreement. • ?sentence([the, student, eats],[],Num,T) • Num = singular, T = s(np(definite,[],student,nopp),vp(eat,nonp,nopp)).

  11. NL Interface to a DB • The query… • ?noun_phrase([a, female, student, enrolled, in, a, computer,science,course]) • …returns C = [course(X), dept(X,comp_science), enrolled(P,X), student(P), female(P)]. • This is (almost) a Datalog query. • See nl_interface.pl

  12. Limitations • Assumptions of compositionality. • Ambiguity of NL: need to deal with uncertainty.

  13. Canned Text Output • A grammar rule can be used “backwards.” • The “meaning” (parse tree) may be bound, while the sentence itself is a free variable. • Example: trans.pl (Figure 3.7) with query… • ?trans (scheduled(w92,cs422,clock(15,30), above(csci333)), T, []). • …produces the answer T = [the, winter, 1992, session, of, the, advanced, artificial, intelligence, course, is, scheduled, at, 3, :, 30, pm, in, the, room, above, the, computer, science, department, office].

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