1. CHR as grammar formalism A first report Henning Christiansen Roskilde University, D ENMARK http://www.dat.ruc.dk/~henning Idea: Propagation rules of CHR: a natural bottom evaluator A bottom-up analogy to Definite Clause Grammars... and see what else CHR can offer to language processing

2. Example: ”Peter likes Mary” np(N0,N1), verb(N1,N2), np(N2,N3) ==> sentence(N0,N3). token(peter,N0,N1) ==> np(N0,N1). token(mary, N0,N1) ==> np(N0,N1). token(likes,N0,N1) ==> verb(N0,N1). token(peter,0,1) token(likes,1,2) token(mary,2,3) sentence(0,3) / | \ np(0,1) verb(1,2) np(2,3) / | \

3. Example: ”Peter likes Mary” np(N0,N1), verb(N1,N2), np(N2,N3) ==> sentence(N0,N3). token(peter,N0,N1) ==> np(N0,N1). token(mary, N0,N1) ==> np(N0,N1). token(likes,N0,N1) ==> verb(N0,N1). Principle obviously correct for grammars without empty productions and loops Robust of errors Attributes can be added as in DCG (of course) Ambiguity not a problem Naive, elegant, but is it useful??

4. A few facts about CHR Declarative language for writing constraint solvers [Frühwirth, 1995] Propagation rules adds:... ==>... Simplification rules replaces:...... + variations and misc. tools Recognized as general purpose logic programming language, e.g.: [Abdennadher, Schütz, 1998]: Combine bottom-up & top-down [Abdennadher, Christiansen, 2000]: ”Natural embedding” of abduction and integrity constraints This talk: Exercises using CHR for language processing

5. Time complexity of prop. rule parsers No backtrack => no combinat. explosion in case of errors n 3 for Chomsky Normal Form grammars [McAllester, 2000; Cocke-Younger-Kasami...] ; in general worse Special problem: local ambiguity => large amount of (perhaps) useless constraints (e.g. A::= a | A A) Several ways to get rid of n 3....

6. Improving time complexity Example: (grammar for arithmetic expressions) exp(N0,N1),token(+,N1,N2),exp(N2,N3) ==> exp(N0,N3).

7. Improving time complexity Make all but rightmost symbol passive exp(N0,N1)#Id1, token(+,N1,N2)#Id2, exp(N2,N3) ==> exp(N0,N3), pragma passive(Id1), passive(Id2).

8. Improving time complexity Make all but rightmost symbol passive Use look-ahead exp(N0,N1)#Id1,token(+,N1,N2)#Id2,exp(N2,N3)#Id3, token(R,N3,N4) ==> member(R,[+,')',eof]) | exp(N0,N3), pragma passive(Id1), passive(Id2), passive(Id3).

9. Improving time complexity Make all but rightmost symbol passive Use look-ahead Use simplification/simpagation rules instead token(R,N3,N4) \ exp(N0,N1)#Id1,token(+,N1,N2)#Id2,exp(N2,N3)#Id3 member(R,[+,')',eof]) | exp(N0,N3), pragma passive(Id1), passive(Id2), passive(Id3).

10. Improving time complexity Make all but rightmost symbol passive Use look-ahead Use simplification/simpagation rules instead

11. Improving time complexity Make all but rightmost symbol passive –does not change grammar; can be added even without grammar-writer’s attention Use look-ahead –as above Use simplification/simpagation rules instead –does not change unambigous grammars –a feature to enforce unambiguity Time complexity not a problem! ”Reasonable” grammars run almost linearly A variety of tools available for the competent CHR grammar writer

12. Fact: CHR perfect tool for passing round hypothesis Example: produce:... ==>... h(X)... apply:... h(X)... ==>... X... Let’s try Assumption Grammars [Dahl, Tarau, Li, 1997] anaphora, coordination, etc.

13. Assumption Grammars +h(a)assert linear hypothesis h(a) for subsequent text *h(a)assert ”intuitionistic” hypothesis for subsequent text -h(X)consume/apply hypothesis =+h(a), =*h(X), =-h(X) as above but free-order (NB: syntax changed slightly compared with [Dahl, Tarau, Li, 1997])

14. Assumption Grammars in CHR Represent=+h(a) as =+(h,[a]), etc. +h(a) as +(h,[a], position ), etc. Implement by following CHR rules: =+(P,A), =-(P,B) true & A=B | true. =*(P,A) \ =-(P,B) true & A=B | true. +(P,A,Z1), -(P,B,Z2) Z1 < Z2 & A=B | true. *(P,A,Z1)\ -(P,B,Z2) Z1 < Z2 & A=B | true. Problem with commit- ment and ambiguity So assume no ambiguity for the moment

15. Example: Anaphora, ”Peter... he...” token(he,N0,N1) pronoun(masc,N0,N1). pronoun(Gender,N0,N1) -(active_individual,[X,Gender],N0), np(X,Gender,N0,N1). token(peter,N0,N1) proper_name(peter,masc,N0,N1). proper_name(X,Gender,N0,N1) *(active_individual,[X,Gender],N0), np(X,Gender,N0,N1).

16. Example: Coordination (adapted from[Dahl, Tarau, Li, 1997]) ”Mary likes and Martha hates Peter” Rule for sentence asking around for a subject token(and,N3,N4) \ np(Sub,N1,N2), verb(V,N2,N3) =-(ref_object,[Obj]), sent(V*(Sub,Obj),N1,N3). The following rule instance applied for sample sentence =+( ref_object,[peter]), =-( ref_object,[ Obj] ) true & [peter]= [Obj] | true. Rule sentence offering its objects to context sent(S1,N1,N2), token(and,N2,N3), sent(V2*(Sub2,Obj2),N3,N4) =+(ref_object,[Obj2]), sent(S1+V2*(Sub2,Obj2),N1,N4).

17. Problems with Assumption Grammars Better control of scope of hypotheses needed, e.g. ”offered subjects” only in single period Priority of hypothesis, pruning... heuristics, weights, fuzzy? Claim: Can be approached in CHR suggestions for new mechanisms can be implemented in CHR... or simply program add hoc!

18. Abduction with integrity constraints (sketch) Consider DCG rule: A --> B 1, B 2, B 3, { P } Under suitable conditions equivalent with B 1 (N0,N1), B 2 (N1,N2), B 3 (N1,N3) P, A (N0,N3). Notice: Our implementation of Assumption Gram’s example of abduction with integrity constraints Integrity constraints reject inconsistent hyp. sets, e.g. *(active_individual,[X,masc]), *(active_individual,[X,fem]) fail. fact(likes,X,Y), fact(hates,X,Y) fail.

19. Problems with abduction + int. constr’s Only one out of several hypotheses chosen Inconsistent choices leads to total failure Attempt to handle ambiguity by propagation rules (==>) will mix up different interpretations of the text Under development: Indexing techniques: Maintain different but shared hypotheses sets in same constraint store Replace ”... fail ” by ”... vanish( index ) ” Our point here: These problems can be approached in CHR!!

20. Problems shared with ”logical” approaches: n m different choices in: Peter 1, Peter 2,..., Peter n... he 1,..., he m Lack of heuristics, scoping, weights... ( as mentioned) Our point: such problems can be approached in general CHR programming framework

21. Conclusion Current work: Indexing and sharing techniques Recognition of NPs for ontology-based search in text DBs; in parallel with ”trad’l approach” in the OntoQuery project Adapt grammar for large subset of Danish; with CST, Copenhagen, also in OntoQuery... and fun to play with too! Simple, naive and neat approach Efficient and robust b.u. parsers Potentiality for powerful and flexible natural language processing methods Suggestive application of declarative constraint programming