Logistic linguistics Bengt 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

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

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

Släktträd/nätverk Philip partner to Vera \/ child to / | \ Bill partner to Una Karin Thomas part to Gerd \/ \/ child to child to /\/ \ John MariaCharlesAnne

Släktträd/nätverk

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']

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]

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]

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,’,’]]

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).

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

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

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

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,.]

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

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

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,.]

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

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?