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LING 388: Language and Computers Sandiway Fong Lecture 15: 10/18.

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Presentation on theme: "LING 388: Language and Computers Sandiway Fong Lecture 15: 10/18."— Presentation transcript:

1 LING 388: Language and Computers Sandiway Fong Lecture 15: 10/18

2 Administrivia Homework #3 –due today – (by

3 Last Time Predicate-Argument Structure –?- s(X, [i,hit,the,ball], []). X = s(np(i),vp(v(hit),np(det(the),n(ball)))) X = hit(i,ball) Calling Prolog code from grammar rules –{... } –headof(X,H) ?- headof(vp(v(hit),np(det(the),n(ball))),V). X = hit –counting a + b + => a n b n

4 Today’s Topics One step towards a simple language translator... –Grammar for Japanese –Differences between English and Japanese Canonical Word Order Wh-questions

5 Japanese Head-Final Language –introduced the notion “head of a phrase” last lecture e.g. verb is the head of a verb phrase –hit the ball –ran noun is the head of a noun phrase –the man (that I saw) –the old man –John’s mother –Sentence word order(canonical) Subject Object Verb –cf. English word order Subject Verb Object

6 Japanese Head-Final Language –Sentence word order(canonical) Subject Object Verb –cf. English word order Subject Verb Object Example: –John bought a book –John a book bought(Japanese word order) –Taroo-ga hon-o katta –ga = nominative case marker –o = accusative case marker

7 Japanese Head-Final Language –Sentence word order(canonical) Subject Object Verb –Japanese also allows “scrambling” e.g. object and subject can be switched in order Subject Object Verb Object Subject Verb *Subject Verb Object(still head-final) Example: –John bought a book –John a book bought –Taroo-ga hon-o katta –hon-o Taroo-ga katta –*Taroo-ga katta hon-o(English word order) –ga = nominative case marker –o = accusative case marker

8 Japanese Example: –John bought a book –John a book bought –Taroo-ga hon-o katta –ga = nominative case marker –o = accusative case marker Input (Prolog list): –[taroo,ga,hon,o,katta] Basic parser rules: –s(s(Y,Z)) --> np(Y), nomcase, vp(Z). –vp(vp(Z,Y)) --> np(Z), acccase, transitive(Y). –transitive(v(katta)) --> [katta]. –nomcase --> [ga]. –acccase --> [o]. –np(np(taroo)) --> [taroo]. –np(np(hon)) --> [hon].

9 Japanese Example: –John a book bought –Taroo-ga hon-o katta –ga = nominative case marker –o = accusative case marker Computation tree: –?- s(X,[taroo,ga,hon,o,katta],[]). ?- np(Y,[taroo,ga,hon,o,katta],L1). ?- nomcase(L1,L2). ?- vp(Z,L2,[]). –?- np(Y,[taroo,ga,hon,o,katta],L1). Y = np(taroo)L1 = [ga,hon,o,katta] –?- nomcase([ga,hon,o,katta],L2). L2 = [hon,o,katta] –?- vp(vp(Z’,Y’), [hon,o,katta],[]). Z = vp(Z’,Y’) ?- np(Z’,[hon,o,katta],L1’). ?- acccase(L1’,L2’). ?- transitive(Y’,L2’,[]). 1.s(s(Y,Z)) --> np(Y), nomcase, vp(Z). 2.vp(vp(Z,Y)) --> np(Z), acccase, transitive(Y). 3.transitive(v(katta)) --> [katta]. 4.nomcase --> [ga]. 5.acccase --> [o]. 6.np(np(taroo)) --> [taroo]. 7.np(np(hon)) --> [hon].

10 Japanese Example: –John a book bought –Taroo-ga hon-o katta –ga = nominative case marker –o = accusative case marker Computation tree: –?- vp(vp(Z’,Y’), [hon,o,katta],[]). ?- np(Z’,[hon,o,katta],L1’). ?- acccase(L1’,L2’). ?- transitive(Y’,L2’,[]). –?- np(Z’,[hon,o,katta],L1’) Z’ = np(hon)L1’ = [o,katta] –?- acccase([o,katta],L2’). L2’ = [katta] –?- transitive(Y’,[katta],[]). Y’ = v(katta) Answer –?- s(X,[taroo,ga,hon,o,katta],[]). –X = s(np(taroo), vp(np(hon), v(katta))) 1.s(s(Y,Z)) --> np(Y), nomcase, vp(Z). 2.vp(vp(Z,Y)) --> np(Z), acccase, transitive(Y). 3.transitive(v(katta)) --> [katta]. 4.nomcase --> [ga]. 5.acccase --> [o]. 6.np(np(taroo)) --> [taroo]. 7.np(np(hon)) --> [hon].

11 Japanese Example: –John a book bought –Taroo-ga hon-o katta –ga = nominative case marker –o = accusative case marker Simple grammar –can be run “backwards” for sentence generation Query –?- s(s(np(taroo), vp(np(hon), v(katta))),L,[]). –L = [taroo, ga, hon, o, katta] 1.s(s(Y,Z)) --> np(Y), nomcase, vp(Z). 2.vp(vp(Z,Y)) --> np(Z), acccase, transitive(Y). 3.transitive(v(katta)) --> [katta]. 4.nomcase --> [ga]. 5.acccase --> [o]. 6.np(np(taroo)) --> [taroo]. 7.np(np(hon)) --> [hon]. Generator SentenceParse tree Parser SentenceParse tree

12 Japanese Example: –John a book bought –Taroo-ga hon-o katta –ga = nominative case marker –o = accusative case marker Query (Generation): –?- s(s(np(taroo),vp(np(hon),v(katta))),L,[]). Y = np(taroo)Z = vp(np(hon),v(katta))) ?- np(np(taroo),L,L1). ?- nomcase(L1,L2). ?- vp(vp(np(hon),v(katta))),L2,[]). –?- np(np(taroo),L,L1). L = [taroo|L1] –?- nomcase(L1,L2). L1 = [ga|L2] –?- vp(vp(np(hon),v(katta))),L2,[]). Z’ = np(hon)Y’ = v(katta) ?- np(np(hon),L2,L3). ?- acccase(L3,L4). ?- transitive(v(katta),L4,[]). 1.s(s(Y,Z)) --> np(Y), nomcase, vp(Z). 2.vp(vp(Z,Y)) --> np(Z), acccase, transitive(Y). 3.transitive(v(katta)) --> [katta]. 4.nomcase --> [ga]. 5.acccase --> [o]. 6.np(np(taroo)) --> [taroo]. 7.np(np(hon)) --> [hon].

13 Japanese Example: –John a book bought –Taroo-ga hon-o katta –ga = nominative case marker –o = accusative case marker Query (Generation): –?- vp(vp(np(hon),v(katta))),L2,[]). Z’ = np(taroo)Y’ = v(katta) ?- np(np(hon),L2,L3). ?- acccase(L3,L4). ?- transitive(v(katta),L4,[]). –?- np(np(hon),L2,L3). L2 = [hon|L3] –?- acccase(L3,L4). L3 = [o|L4] –?- transitive(v(katta),L4,[]). L4 = [katta|[]] 1.s(s(Y,Z)) --> np(Y), nomcase, vp(Z). 2.vp(vp(Z,Y)) --> np(Z), acccase, transitive(Y). 3.transitive(v(katta)) --> [katta]. 4.nomcase --> [ga]. 5.acccase --> [o]. 6.np(np(taroo)) --> [taroo]. 7.np(np(hon)) --> [hon].

14 Japanese Example: –John a book bought –Taroo-ga hon-o katta –ga = nominative case marker –o = accusative case marker Query (Generation): –back-substituting... –?- np(np(taroo),L,L1). L = [taroo|L1] –?- nomcase(L1,L2). L1 = [ga|L2] –?- np(np(hon),L2,L3). L2 = [hon|L3] –?- acccase(L3,L4). L3 = [o|L4] –?- transitive(v(katta),L4,[]). L4 = [katta|[]] Answer: –L = [taroo, ga, hon, o, katta] 1.s(s(Y,Z)) --> np(Y), nomcase, vp(Z). 2.vp(vp(Z,Y)) --> np(Z), acccase, transitive(Y). 3.transitive(v(katta)) --> [katta]. 4.nomcase --> [ga]. 5.acccase --> [o]. 6.np(np(taroo)) --> [taroo]. 7.np(np(hon)) --> [hon].

15 Japanese Wh-Phrases –English John bought a book Who bought a book?(subject wh-phrase) *John bought what?(only possible as an echo- question) What did John buy?(object wh-phrase) –Complex operation: (irregular) »object wh-phrase must be fronted »do-support (insertion of past tense form of “do”) »bought  buy (untensed form)

16 Japanese Wh-Phrases –English Who bought a book?(subject wh-phrase) *John bought what?(only possible as an echo-question) What did John buy?(object wh-phrase) –Japanese wh-in-situ: –meaning wh-phrase appears in same position as a regular noun phrase –easy to implement! Taroo-ga nani-o katta ka –nani: means what –ka: sentence-final question particle dare-ga hon-o katta ka –dare: means who

17 Japanese wh-in-situ: –Taroo-ga nani-o katta ka nani: means what ka: sentence-final question particle –dare-ga hon-o katta ka dare: means who Grammar: –s(s(Y,Z)) --> np(Y), nomcase, vp(Z). –vp(vp(Z,Y)) --> np(Z), acccase, transitive(Y). –transitive(v(katta)) --> [katta]. –nomcase --> [ga]. –acccase --> [o]. –np(np(taroo)) --> [taroo]. –np(np(hon)) --> [hon]. Add new wh-words: –np(np(dare)) --> [dare]. –np(np(nani)) --> [nani].

18 Japanese wh-in-situ: –Taroo-ga nani-o katta ka nani: means what ka: sentence-final question particle –dare-ga hon-o katta ka dare: means who Grammar: –s(s(Y,Z)) --> np(Y), nomcase, vp(Z). –vp(vp(Z,Y)) --> np(Z), acccase, transitive(Y). –transitive(v(katta)) --> [katta]. –nomcase --> [ga]. –acccase --> [o]. –np(np(taroo)) --> [taroo]. –np(np(hon)) --> [hon]. –np(np(dare)) --> [dare]. –np(np(nani)) --> [nani]. Allows sentences: –Taroo-ga hon-o katta –Taroo-ga nani-o katta (ka) –dare-ga hon-o katta (ka) How do we enforce the constraint that ka is obligatory when a wh-phrase is in the sentence?

19 Japanese wh-in-situ: –Taroo-ga nani-o katta ka nani: means what ka: sentence-final question particle –dare-ga hon-o katta ka dare: means who Grammar: –s(s(Y,Z)) --> np(Y,Q), nomcase, vp(Z). –vp(vp(Z,Y)) --> np(Z,Q), acccase, transitive(Y). –transitive(v(katta)) --> [katta]. –nomcase --> [ga]. –acccase --> [o]. –np(np(taroo),notwh) --> [taroo]. –np(np(hon),notwh) --> [hon]. –np(np(dare),wh) --> [dare]. –np(np(nani),wh) --> [nani]. Answer: –Use an extra argument to encode the lexical feature wh for nouns

20 Japanese wh-in-situ: –Taroo-ga nani-o katta ka nani: means what ka: sentence-final question particle –dare-ga hon-o katta ka dare: means who Grammar: –s(s(Y,Z)) --> np(Y,Q1), nomcase, vp(Z,Q2). –vp(vp(Z,Y),Q) --> np(Z,Q), acccase, transitive(Y). –transitive(v(katta)) --> [katta]. –nomcase --> [ga]. –acccase --> [o]. –np(np(taroo),notwh) --> [taroo]. –np(np(hon),notwh) --> [hon]. –np(np(dare),wh) --> [dare]. –np(np(nani),wh) --> [nani]. Answer: –Use an extra argument to encode the lexical feature wh for nouns –Propagate this feature up to the sentence rule

21 Japanese wh-in-situ: –Taroo-ga nani-o katta ka nani: means what ka: sentence-final question particle –dare-ga hon-o katta ka dare: means who Grammar: –s(s(Y,Z)) --> np(Y,Q1), nomcase, vp(Z,Q2), sf(Q1,Q2). –vp(vp(Z,Y),Q) --> np(Z,Q), acccase, transitive(Y). –transitive(v(katta)) --> [katta]. –nomcase --> [ga]. –acccase --> [o]. –np(np(taroo),notwh) --> [taroo]. –np(np(hon),notwh) --> [hon]. –np(np(dare),wh) --> [dare]. –np(np(nani),wh) --> [nani]. Answer: –Use an extra argument to encode the lexical feature wh for nouns –Propagate this feature up to the sentence rule –Add a sentence-final particle rule that generates ka when this feature is wh

22 Japanese wh-in-situ: –Taroo-ga nani-o katta ka nani: means what ka: sentence-final question particle –dare-ga hon-o katta ka dare: means who Grammar: –s(s(Y,Z)) --> np(Y,Q1), nomcase, vp(Z,Q2), sf(Q1,Q2). –vp(vp(Z,Y),Q) --> np(Z,Q), acccase, transitive(Y). –transitive(v(katta)) --> [katta]. –nomcase --> [ga]. –acccase --> [o]. –np(np(taroo),notwh) --> [taroo]. –np(np(hon),notwh) --> [hon]. –np(np(dare),wh) --> [dare]. –np(np(nani),wh) --> [nani]. Sentence-final particle rule: –sf(wh,notwh) --> [ka]. –sf(notwh,wh) --> [ka]. –sf(notwh,notwh) --> []. (empty) –sf(wh,wh) --> [ka].( dare-ga nani-o katta ka: who bought what )

23 Japanese wh-in-situ: –Taroo-ga nani-o katta ka nani: means what ka: sentence-final question particle –dare-ga hon-o katta ka dare: means who Computation tree: –?- s(X,[taroo,ga,nani,o,katta,ka],[]). –X = s(np(taroo),vp(np(nani),v(katta))) –?- s(X,[taroo,ga,nani,o,katta],[]). –no s(s(Y,Z)) --> np(Y,Q1), nomcase, vp(Z,Q2), sf(Q1,Q2). vp(vp(Z,Y),Q) --> np(Z,Q), acccase, transitive(Y). transitive(v(katta)) --> [katta]. nomcase --> [ga]. acccase --> [o]. np(np(taroo),notwh) --> [taroo]. np(np(hon),notwh) --> [hon]. np(np(dare),wh) --> [dare]. np(np(nani),wh) --> [nani]. sf(wh,notwh) --> [ka]. sf(notwh,wh) --> [ka]. sf(notwh,notwh) --> []. sf(wh,wh) --> [ka]. We may want to modifiy the parse tree to represent the sentence-final particle ka as well

24 Next Time We’ll look at wh-questions in English... and also take another step towards our machine translator


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