Logistic linguistics Bengt Sigurd & Mats Eeg-Olofsson

1. Logistic linguisticsBengt Sigurd & Mats Eeg-Olofsson From first sound/morpheme/word to last via shorter combinable roads (dyads) Logistic syllable analysis: st-tr-ra-an-nd Logistic kinship analysis: x,kusin,w :- x,barn,y,y,syskon,z,z,föräld,w Logistic morphology:o,be-be,slut-slut,sam Logistic text generation bo-pengar: bo,känner,leif,flyger,helikopter, landar,tak,har,värdedepå,har,pengar

2. Dyads in onset + coda in syllables • [[s,t],[t,r],[r,a]]+[[a,n],[n,d]] % strand • [[t,r],[r,a]]+[[a,s],[s,t]] % trast • [[s,t],[t,a]]+[[a,r,[r,t]] % start • [[s,p],[p,r],[r,e]]+[[e,l][l,s]] % sprels

3. Syllable duration derived and measured • Measured data predicted values • dur([p,e,l],538). 541 • dur([s,p,e,l],743). 741 • dur([p,e,l,s],805). 729 • dur([r,e,l],536). 527 • dur([p,r,e],552). 568 • dur([s,p,r,e,l],835). 832 • dur([s,p,r,e,l,s],1028). 1020

4. Släktträd/nätverk Philip partner to Vera • \ / • child to • / | \ • Bill partner to Una Karin Thomas part to Gerd • \ / \ / child to child to / \ / \ • John Maria Charles Anne

5. Släktträd/nätverk

6. English demos erel(A,B,C,D)How are A and B related? • No.1 : A = 'John', B = 'Charles’,C = 3, D = ['John', cousin, to, 'Charles’] • No.20 : A = 'John', B = 'Maria', C = 1, D = ['John', sibling, to, 'Maria'] • No.44 : A = 'Una', B = 'Karin', C = 1, D = ['Una', sister, to, 'Karin'] • No.102 : A = 'John', B = 'Karin', C = 2, D = ['John', nephew, to, 'Karin'] • No.109 : A = 'Maria', B = 'Thomas', C = 2, D = ['Maria', niece, to, 'Thomas']

7. word(A, B, C, D) generating more or less comprehensible words No.1 : A = o, B = trött, C = 2, D = [o, trött] No.1 : A = o, B = sam, C = 3, D = [o, akt, sam] No.2 : A = för, B = sam, C = 3, D = [för, trött, sam] No.3 : A = o, B = lig, C = 4, D = [o, för, son, lig] No.4 : A = o, B = sam, C = 4, D = [o, för, akt, sam] No.5 : A = o, B = het, C=5, D = [o, för, son, lig,het] No.6 : A = o, B = het, C = 6, D = [o, för, be, akt,sam, het]

8. rel(A,B, C, D) % looking for relations sten-maria,bertil-pengar • rel(sten,maria,C,D) • No.1 : C = 5, D = [sten, gör, ibland, smuggling, vanligt, i, hamn, finns, i, malmö, hemstad, för, per, känner, nog, maria] •  rel(bertil, pengar, C, D), C>8 • No.1 : C = 12, D = [bertil, förälder, till, jarl, bodde, i, malmö, hemstad, för, per, känner, nog, maria, arbetade, på, bonniers, hyste, tidvis, jacob, gick, på, sigtuna, hyste, tidvis, leif, flyger, ibland, helikopter, landar, på, tak, finns, på, depå, ger, ofta, pengar]

9. Dyads of grammatical categories in onset + coda in Logistic grammar • [[hunden,bet]]+[[bet,inte],[inte,råttan],[råttan,.]] • [[N,Vt]]+[[Vt,Ne],[Ne,N],[N,’.’]] • Subordinate clause • (att) [[hunden,inte],[inte,bet]]+[bet,råttan],[råttan,’,’]] • [[N,Ne],[Ne,Vt]]+[Vt,N],[N,’,’]]

10. Prolog for sents as onset + coda • sents(X,Z,C3,D3) :- oo(X,Y,C,D), • cc(Y,Z,C2,D2),D2=[H|T],append(D,T,D3), • C3 is C + C2. % sats består av onset(D) samt coda(D2) som har verb som brygga • Onset rules (dyads) • o(N,V,1,[N,V]) :- np(N),v(V). • o(N,V,1,[N,V]) :- np(N),vt(V). • o(N,V,1,[N,V]) :- np(N),aux(V).

11. Sent codas • c(V,'.',1,[V,'.']) :- v(V). % final v med punkt • c(V,N,1,[V,N]) :- vt(V),np(N). % bet hund • c(N,'.',1,[N,'.']) :- np(N). % final obj n med . • c(N,A,1,[N,A]) :- np(N),adv(A). % obj n +A • c(A,'.',1,[A,'.']) :- adv(A). % final adv med . • c(V,Ne,1,[V,Ne]) :- vt(V),neg(Ne). % bet inte • c(Ne,A,1,[Ne,A]) :- neg(Ne),adv(A). % inte A • c(Ne,N,1,[Ne,N]) :- neg(Ne),np(N). % inte hund • c(X,Y,1,[X,Y]) :- aux(X),inf(Y). % kan komma

12. Lexicon • n(hunden). • n(katten). • n(gatan). • rel(som). • v(föll). • v(kom). • vt(bet). • adv(snabbt).

13. Lexicon • c(när). • p(på). • aux(kan). • inf(komma). • neg(inte). • np(N) :- n(N). • adv([P,N]) :- p(P),np(N).

14. Demos main sents • sents(A, ., C, D) % final punkt required • No.1 : A = hunden, C = 2, D = [hunden, föll, .] • No.2 : A = katten, C = 3, D = [katten, bet, hunden, .] • No.3 : A = katten, C = 4, D = [katten, bet, hunden, snabbt, .] • No.7 : A = katten, C = 4, D = [katten, bet, hunden, [på, gatan], .] • No.14 : A = hunden, C = 4, D = [hunden, bet, inte, snabbt, .]

15. Np med rel, Adv clauses np(Np) :- n(N),sentr(A,B,C,D),append([N],D,Np). % N with subj relative clause np(Np) :- n(N),sentro(A,B,C,D),append([N],D,Np). % N with obj relative clause adv(D2) :- c(Cu),sentu(A,B,C,D),append([Cu],D,D2).% conjunc with sub clause

16. Inverted word order • oi(A,B,1,[A,B]) :- adv(A),v(B). % snabbt föll • oi(A,B,1,[A,B]) :- adv(A),vt(B). % snabbt bet • ci(N,'.',1,[N,'.']) :- n(N). % (snabbt föll) n med . • ci(V,N2,1,[V,N,N2]) :- vt(V),n(N),n(N2). % (snabbt) bet katt hund • ci(V,N2,1,[V,N,Ne,N2]) :- vt(V),n(N),neg(Ne),n(N2). % (snabbt) bet katt inte hund • ci(N,A,1,[N,A]) :- n(N),adv(A). % (bet) hund snabbt

17. Demos inverted • senti(A, ., C, D) • No.1 : A = snabbt, C = 3, D = [snabbt, föll, hunden, .] • No.6 : A = snabbt, C = 4, D = [snabbt, föll, hunden, [på, gatan], .] • No.13 : A = [på, gatan], C = 3, D = [[på, gatan], föll, hunden, .] • No.30 : A = snabbt, C = 4, D = [snabbt, bet, katten, hunden, [på, gatan], .] • No.31 : A = snabbt, C = 3, D = [snabbt, bet, katten, inte, hunden, .]

18. Demo with rel and adv clause sents(A, B, C, [hunden, som, kom, föll, .]) • No.1 : A = hunden, B = ., C = 5 • sents(A, B, C, [katten, som, föll, bet, inte, hunden, snabbt, .]) • No.1 : A = katten, B = ., C = 8 • sents(A, B, C, [hunden, som, katten, bet,[när,hunden,föll], föll, .]) • No.1 : A = hunden, B = ., C = 5 • sents(A, B, C, [hunden, som, katten, bet, bet, katten, .]) • No.1 : A = hunden, B = ., C = 6

19. Conclusions • It is possible to describe (generate) all(?) types of sentences by logistic grammar Can one scale-up the test grammar adding e.g. coordination and using available lexicons? Does logistic grammar offer new interesting typological possibilities? Does logistic grammar offer new pedagogical possibilities? Can one predict the processing and duration of sentences by logistic linguistics? How does logistic grammar relate to other types of grammar?