1. LING 438/538 Computational Linguistics Sandiway Fong Lecture 23: 11/20

2. Today’s Topics Three things 1.continue with context-free grammar example deal with left recursion problem... 2.Homework your chance to write a context-free grammar 538 Class Presentations selecting a chapter format etc.

3. Last Time Let’s write a context-free grammar that returns parse trees for simple active/passive sentence pairs such as: –John hit a ball/John ate a sandwich –*John hit/John ate –*hit a ball/*ate a sandwich –the ball was hit/the sandwich was eaten –the ball was hit by John/the sandwich was eaten by John Let’s introduce traces in the case of passives: –[ S [ NP the ball] [ VP [aux was ][ VP [ V hit] [ NP trace]]]] –[ S [ NP the ball] [ VP [ VP [aux was ][ VP [ V hit] [ NP trace]]][ PP [ P by][ NP John]]]]

4. Grammar Note: –need to handle English passive morphology –passive be selects for a V-en Example –*was ate (simple past form) –was eaten (-en past participle form) Implementation: –use an extra argument to indicate the verb form –v(v(ate),past) --> [ate]. –v(v(eaten),pastparticiple) --> [eaten].

5. Grammar [Developed in class] s(s(NP,VP)) --> np(NP,notrace), vp(VP,_,_,notrace). np(np(Det,N),notrace) --> det(Det), common_noun(N). np(np(N),notrace) --> proper_noun(N). np(np(trace),trace) --> []. proper_noun(john) --> [john]. det(det(the)) --> [the]. det(det(a)) --> [a]. common_noun(n(ball)) --> [ball]. common_noun(n(sandwich)) --> [sandwich]. vp(vp(BE,VP),Form,selectsforvp,no trace) --> passive_be(BE,Form), vp(VP,pastparticiple,transitive,tr ace). vp(vp(V,NP),Form,transitive,EC) --> transitive(V,Form), np(NP,EC). vp(vp(V),Form,intransitive,notrace) --> intransitive(V,Form). vp(vp(VP,PP),Form,transitive,EC) - -> vp(VP,Form,transitive,EC), pp(PP).

6. Grammar passive_be(v(be),root) --> [be]. passive_be(v(is),thirdpersonpresen t) --> [is]. passive_be(v(was),past) --> [was]. intransitive(v(eat),root) --> [eat]. intransitive(v(eats),s) --> [eats]. intransitive(v(ate),past) --> [ate]. intransitive(v(eaten),pastparticiple) --> [eaten]. transitive(v(eat),root) --> [eat]. transitive(v(eats),s) --> [eats]. transitive(v(ate),past) --> [ate]. transitive(v(eaten),pastparticiple) -- > [eaten]. transitive(v(hit),root) --> [hit]. transitive(v(hits),s) --> [hits]. transitive(v(hit),past) --> [hit]. transitive(v(hit),pastparticiple) --> [hit]. pp(pp(P,NP)) --> p(P), np(NP,notrace). p(p(by)) --> [by].

7. Grammatical Sentences –John hit a ball/John ate a sandwich –John ate –the ball was hit/the sandwich was eaten –the ball was hit by John/the sandwich was eaten by John ?- s(X,[john,hit,the,ball],[]). X = s(np(john),vp(v(hit),np(det(the),n(ball)))) | ?- s(X,[john,ate,a,sandwich],[]). X = s(np(john),vp(v(ate),np(det(a),n(sandwich)))) | ?- s(X,[john,ate],[]). X = s(np(john),vp(v(ate))) | ?- s(X,[the,sandwich,was,eaten],[]). X = s(np(det(the),n(sandwich)),vp(v(was),vp(v(eaten),np(trace)))) | ?- s(X,[the,sandwich,was,eaten,by,john],[]). X = s(np(det(the),n(sandwich)),vp(v(was),vp(vp(v(eaten),np(trace)),pp(p(by),np(joh n)))))

8. Infinite loop Occurs with ungrammatical input –*John hit Also with grammatical input when we ask for more solutions –i.e. invoke backtracking –John ate a sandwich Computational System –involves recursion –Prolog also selects first matching rule –but will try other rules on backtracking

9. Solving the Left Adjunction Problem Rule (simplified): –vp --> vp, pp. –causes Prolog to go into an infinite loop Why? –Suppose there is no PP in the input –what happens on backtracking?

10. Solving the Left Adjunction Problem Idea: –Look ahead into the input for a potential PP –License Prolog to use the VP adjunction rule only when there is an appropriate (overt) preposition ahead in the input

11. Solving the Left Adjunction Problem Implementation: –requires access to the input list –not available directly from the DCG rule DCG rules are translated into underlying Prolog rules that contain input/output list pairs Example: DCG rule –vp(vp(VP,PP)) --> vp(VP,Number), pp(PP). –gets translated into Prolog as –vp(vp(A,B), C, D, E) :- vp(A, C, D, F), pp(B, F, E). –D = part of sentence to be analyzed by the VP rule –E = part left over after VP rule –(the sandwich) D = [was,eaten,by,john] –E = [] –F = ?

12. Solving the Left Adjunction Problem DCG rules are translated into underlying Prolog rules that contain input/output list pairs Example: DCG rule –vp(vp(VP,PP)) --> vp(VP,Number), pp(PP). –gets translated into Prolog: –vp(vp(A,B), C, D, E) :- vp(A, C, D, F), pp(B, F, E). Solution: –modify the underlying Prolog rule directly –add a call to a Prolog predicate to check for list membership for preposition –vp(vp(A,B), C, D, E) :- checkforpp(D), vp(A, C, D, F), pp(B, F, E).

13. Solving the Left Adjunction Problem Prolog VP adjunction rule –vp(vp(A,B), C, D, E) :- checkforpp(D), vp(A, C, D, F), pp(B, F, E). Implementation of the supporting predicate checkforpp/1 : –% checkforpp(List) true if List contains a preposition (by) –checkforpp([by|_]). –checkforpp([_|L]) :- checkforpp(L).

14. Solving the Left Adjunction Problem Actually, this only partially solves the problem Case 1: no PP in input –VP adjunction won’t be triggered because checkforpp/1 fails –vp(vp(A,B), C, D, E) :- checkforpp(D), vp(A, C, D, F), pp(B, F, E). Case 2: there is a PP in input –still get recursion on backtracking... and an infinite loop –because each recursion is licensed by the same PP Idea: –need to say that we license VP adjunction one PP at a time Prolog solution: –each time checkforpp/1 succeeds it should “mark” the PP so that next time it is called it won’t select the same PP again

15. Solving the Left Adjunction Problem Idea: –need to say that we license VP adjunction one PP at a time Prolog solution: –each time checkforpp/1 succeeds it should “mark” the PP so that next time it is called it won’t select the same PP again Implementation: –checkforpp/2 –% checkforpp(List,NewList) true if List contains a preposition (by)and NewList is the marked List –checkforpp([by|L],[by2|L]). –checkforpp([X|L],[X|L2]) :- \+X=by, checkforpp(L,L2).

16. Solving the Left Adjunction Problem VP adjunction Prolog rule: –vp(vp(A,B), C, D, E) :- checkforpp(D,G), vp(A, C, G, F), pp(B, F, E). must make sure PP rules still manage to pick up marked preposition Hence : –p(p(by)) --> [by]. –must morph into: –p(p(by)) --> [by2].

17. Homework due Tuesday 27th

18. Why can’t computers use English? from Lecture 1 –a linguist’s view: a list of examples that are hard for computers to do –a computational linguist’s view (mine): these actually aren’t very hard at all... armed with some DCG technology, we can easily write a grammar to that make the distinctions outlined in the pamphlet –your homework task write a grammar for these examples

19. If computers are so smart, why can't they use simple English? Consider, for instance, the four letters read ; they can be pronounced as either reed or red. How does the machine know in each case which is the correct pronunciation? Suppose it comes across the following sentences: (l) The girls will read the paper. (reed) (2) The girls have read the paper. (red) We might program the machine to pronounce read as reed if it comes right after will, and red if it comes right after have. But then sentences (3) through (5) would cause trouble. (3) Will the girls read the paper? (reed) (4) Have any men of good will read the paper? (red) (5) Have the executors of the will read the paper? (red) How can we program the machine to make this come out right?

20. If computers are so smart, why can't they use simple English? (6) Have the girls who will be on vacation next week read the paper yet? (red) (7) Please have the girls read the paper. (reed) (8) Have the girls read the paper?(red) Sentence (6) contains both have and will before read, and both of them are auxiliary verbs. But will modifies be, and have modifies read. In order to match up the verbs with their auxiliaries, the machine needs to know that the girls who will be on vacation next week is a separate phrase inside the sentence. In sentence (7), have is not an auxiliary verb at all, but a main verb that means something like 'cause' or 'bring about'. To get the pronunciation right, the machine would have to be able to recognize the difference between a command like (7) and the very similar question in (8), which requires the pronunciation red.

21. Homework Requirements This is what you need to submit Part 1 –write down (in English) the grammatical constraints you are going to use to make the distinctions in examples (1) – (8). –e.g. what you are assuming about things like auxiliary/verb fronting and the constraints from perfective have Part 2 –implement your constraints in the framework of a Definite Clause Grammar (DCG) that returns parse trees. –submit both your grammar and the runs. –to make the distinction between the forms of the verb read readily apparent in your parse trees, use something like: –v(v(red),pastparticiple) --> [read]. –v(v(read),root) --> [read].

22. Homework Requirements Note: the question mark is crucial in the following example (5) Have the executors of the will read the paper? (red) Note: –you can either treat ? as an input word or have the parser return two possible parses (without ?)

23. 538 class presentations your chance to get up and explain ideas in computational linguistics to the rest of the class Textbook Chapters: –from Chapter 11 onwards –as long as it’s on material we haven’t covered (or will cover) in class –so, e.g., the basic pumping lemma wouldn’t be acceptable Remaining topics: –parsing techniques (left-corner, chart, tabular) –WordNet (ontologies, semantic networks)

24. Chapters II: Syntax 11 Features and Unification 11 Features and Unification 12 Lexicalized and Probabilistic Parsing12 Lexicalized and Probabilistic Parsing 13 Language and Complexity13 Language and Complexity III: Semantics 14 Representing Meaning14 Representing Meaning 15 Semantic Analysis 16 Lexical Semantics 17 Word Sense Disambiguation and Information Retrieval17 Word Sense Disambiguation and Information Retrieval IV: Pragmatics 18 Discourse 19 Dialog and Conversational Agents19 Dialog and Conversational Agents 20 Natural Language Generation V: Multilingual Processing 21 Machine Translation

25. 538 class presentations Pick one chapter –pick topic(s) within the chapter –send me email: first-come first-served –(same chapter different topics possible) –10 minute presentation with slides –(powerpoint, PDF acceptable) –explain and evaluate the central idea/technique/algorithm/trade-offs behind the topic you’ve chosen –you’ll be graded on clarity of presentation and how well you explain or communicate the topic(s)