1. UNIVERSITY OF SOUTH CAROLINA Department of Computer Science and Engineering Using Definite Knowledge: NLP and nl_interface.pl Notes for Ch.3 of Poole et al. CSCE 580 Marco Valtorta

2. UNIVERSITY OF SOUTH CAROLINA Department of Computer Science and Engineering 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. UNIVERSITY OF SOUTH CAROLINA Department of Computer Science and Engineering 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. UNIVERSITY OF SOUTH CAROLINA Department of Computer Science and Engineering 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. UNIVERSITY OF SOUTH CAROLINA Department of Computer Science and Engineering Definite Clause Grammars We may do away with explicit appends by 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. UNIVERSITY OF SOUTH CAROLINA Department of Computer Science and Engineering 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. UNIVERSITY OF SOUTH CAROLINA Department of Computer Science and Engineering 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. UNIVERSITY OF SOUTH CAROLINA Department of Computer Science and Engineering 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. UNIVERSITY OF SOUTH CAROLINA Department of Computer Science and Engineering 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. UNIVERSITY OF SOUTH CAROLINA Department of Computer Science and Engineering 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. UNIVERSITY OF SOUTH CAROLINA Department of Computer Science and Engineering 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. UNIVERSITY OF SOUTH CAROLINA Department of Computer Science and Engineering Limitations Assumptions of compositionality. Ambiguity of NL: need to deal with uncertainty.

13. UNIVERSITY OF SOUTH CAROLINA Department of Computer Science and Engineering 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].