CHAPTER Y.

DEVELOPMENT ~ TRANSLATION ALGORITHM ~ IIaIMPLEHENTATIOB

As discussed ea.r 1 ier, 8. hllm8.n t r8.n l.a.tor l'ecogn ises

various sentence parts on the basis of the typology of

sentence-constituent form8.t ions, In other words" 8.

translator matches a sentential component and/or combination

thereof of the given sentence with the typologies of

sentence parts for the purpose of their identification~

Similarly, identification of different syntactic structures

is carried out by phrase structure analysis," Based on

typology of sentence parts, dicussed in chapter III, it is

possible to' formula.te multiple number of tree-structu.res.

In fa.ct, various types of subjects, predicates, attributes,

object and adverbial modifiers find reflection in the so-

called NP a.nd VP/RVP structures (tra.ditiona.lly known as

subject and predioateof the sentence respectively), and it

becomes pra.ctics.ble to perform mechanica.l tra.ns lat ion of

Russian sentences, including pretty long syntactic

formations, that fit in NP and VP structures involving

different types of subjects, predicates, etc. It is true

that a.ll permutations and combinations of the prima.ries and

secondary sentence parts may not be logically possible (due

to predicativity condition), and this fa.ctor has been taken

care of while developing the translation program.

Given below a.re a. few examples of different parse

105

trees/phrase structure trees which explain natu.ral la.nguage

grammar (here Russian grammar) in the form of context-free

grammar/logic gramma.r, showing forma.l representation of

various sentence parts in the form of phrase structures and

the structure of Russian flentences. (In fa.ct > it is these

parse trees which are to be constructed automatically for

the Russain gra.rnma.r-our parsing problem that we sha.ll

discuss next).

FIG- 1

Gra.mmar:

LT: AT:

s ---) NP VP N NP --) Pro

NP·-:> Aclj N) Pro

NP --) APN (Oli- Adj VP --) VNP AdvP V AP --) Adj Adv AdVP-) Adv Conj Adv Adv

PH..C i

On Sub

he he

v . I 1 Chlt'3. Pred

Conj

A~ I ~ t\Jj< N , . ,

tekstovoi material Attr Obj

read textu8.1 res.d -h\,e textu'3.l

m.aterial material

106

---) It'lateri'3.1 ---) on ---) tekstovoi ---) chital ---) gromko ---) privil'no ---) i

loudly loudly

and correctly and correctly

FIG- 2

Gramms.r:

S ---) NP vp N ---) stlJ.denty NP---> N N ---) ks.nikuly NP---) APN Ad,j ---) zimnye vp---) AdvP V NP V ---) proveli AP---) Ad,j Adv ---) vese~o AdvP-> Adv

s

!\ 'r (N.\.'.,,: ... A1d.i "'\{

~,

zimnye kanikuly

N Adv . --

" ~ )

\ , d

studenty veselo proveli Sub AM Pred Attr Obj

LT: students Rlerri ly spent winter holidays AT: the studen-ts spent winter holidays merrily

107

Gr8.mmar!

S ---)

NP---> NP---> VP---> PP---)

PM I onQ.. Sub

LT: she AT: she

FIG-

NP VP Pro N Aux V PP PP Prep NP

s

~ ....... ~~ I_~-K ,~~/ >~~?

khochet igrat' Pred

wants to p18.y wants to play

108

3

N ---) parke Pro ---) ona Pro ---) n8.mi All X ---) khochet V ---) igra.t' Prep ---) s Prep ---> v

pp

-A . rp l~fj r

P'hOi. PA.O I"· N ,""t' I. .,' / I s nam1 i parke

I Obj AM with us in park with us in the park

Grammar:

LT: AT:

S -_._> NP VP HP---> Pro NP---> Proper N VP--'-> V NP VP vp---) V

.tUJ , "" ",. /./

oni poprosili Sub Pred

they 3.sked they asked

FIG- 4

Proper N ---) IVana. Pro ---) oni V ---) poprosili V ---> udalit'sY8.

PMP'~IN 'I I Ivs.ns. uda.l it' Sy3.

Obj I Obj Ivan to lea.ve Ivan to leave

1139

FIG- 5

Grallll8.r:

S ---) SP VP Proper N ---) Vladillir NP---> Pro Proper I ---> Volgograda NP---> Proper N Pro ---> nail vp---) V pp pp V ---) priekhal pp---> Prep IP Prep ---) k

Prep ---) iz

5

NP

~f\.OrVv N A r~~ NP

J

Vladillir priekhal k i iz nail

Sub Pred I Obj AM LT: Vladimir calle to us froll Volgograd AT: Vladil1ir c8.Ile to us froll Volgograd

110

Grammar:

LT: AT:

S ---) NP VP NP---> N NP---)'APN VP---> Aux V PP PP---) Prep NP AP---) Adj AP AP---) Adj

.. Af At\] ¥J

, t'" t e .1 1nos.rannye Attribute

these foreign these foreign

s

tutisty Sub

tourists tourists

FIG- 6

N ---) turisty N ---} marte Adj ---) e'ti Adj ---) inostra.nnye Aux' ---) khotyat V ---) puteshestvovat' Prep ---) v

pp

il /"". ~ _ J..4

. /1/ '.> ~,j "'J ~

kh6tya.t PI) teshestvovat 'v' marte Predicate AM

want to tra.vel in Ma.rch want to travel in March

111

FIG- 7

Gr~.mrn3.r :

S ---) NP VP N ---)

NP----- > N N ---)

NP---> APM N ---)

VP---> VNP pp Adj ---> pp---> Prep NP Adj ---)

AP---> Adj AP Adj ---)

AP---> Adj V ---)

Prep ---)

s

Ptlih Adj () ,I. J .:> .

e t1 ~nJodye lyudl Attr Suh

IT:these young peopJe accomplished this ~redt feet tor the sa~e of aotherland AT:these '{OUill} people accomplished this ~reat feet for the sakE- of ilotherlan..-l

112

lyudi podvig rodiny e'ti molodye e'tot sovershili r~.di

"

FIG- 8

Gra.lIlma.r:

S ---) NP VP Proper N ---)

NP---) Proper N Proper N ---)

NP---) N N ---> NP---) AP N Adj ---> VP---> AUK V pp pp Adj ---> pp---> Prep NP Aux ---)

AP---> Adj AP V ---)

AP---- > Adj Prep ---)

Prep --->

NP

LT: Russia contillueE. to )lrotest i1qainst this up.justiiied lel!islilticn ill UNO PoT: Russia continues to protest al!ainst this ur.justiiied le~islation in UNO

113 I .

Rossia.. OON' zakonodatel'stva e'togo nespra.vedl ivogo prodolzhaet vozra.ha.t' protiv v

FIG- 9

Grammar:

LT: AT:

s ---) NP VP NP ---) NP1 Conj NP2 NP1-> Nl NP Z-> NP --->

N.Z N

VP ---) V PP PP ---> Prep NP

N P,

knigi i Homogeneous

books and books s.nd

c,,: " S ~; .

kar 8.nd as hi subjects

pencils pencils

s.re are

114

Nl ---> knigi N2 ---) ks.r s.nd as hi N ---> stole V ---) lezh8.t Prep ---) na Conj ---> i

~~

lezhat Pred

lying lying

'" P)tQ.f \-\ P

J I

a stole AM

t8.ble on the table on

FIG- 10

Grammar:

S ---> NP VP N ---> risunki NP---> APN N ---> rasslnotreniya NP---> N . Adj ---> e'ti VP---) VNP Adj ---> n'3.chertannye AP---> t\dj AP Adj ---) ·dal'neishego AP---- > Adv AP V ---) trebu:yut AP---> Adj Adv ---) neyavno

$

Af

~ Aolj /\

Adv AP

I lei·

/'~ , N

Aclj

v

IJ

e ' t i neyavno n'3.chert8.nnye risunki Sub

tr8.ced traced

I trebuyut dal'neishego rassmotreniya

Attribute LT: these poorly AT: these poorly

Pred Attribute I Obj d ia.grams need further eX8.minat ion didagrams need further examination

115

FIG- 11

Gr .s.nm.s.r :

S ---) NP VP N ---) shofar NP---) N N ---> detai VP---> V NP AdvP PP N ---) shkoli AdvP-> Adv V ---) vez PP---> Prep NP Adv ---> domoi

:pb-ep ---) . it'· ;;.. '~ -.. ,

s

v NP

N Adv

shofer vez detei domoi iz shkoli Sub Pred Obj AM AM

LT: chauffeur drove children home from school AT: the ch.s.uffeur drove the children home from the school

116

GralnlUar:

LT: AT:

S NP NP 1 NP 2 NP VP PP AP

dve

two two

---) NP VP --- > NP 1 Con.j NP2"

<)

FIG- 12

Nl ---> sastry -) Numeral Nl -) Numeral N2

N 2 ---) bra.ta. N ---) ministerstve

---) A~ ---) V PP ---) Prep NP

Ad,j ---) e'tom Numeral-> dve Numera.l-> tri

---) Adj

sestry i Sub

sesters 8.nd sesters 8.nd

V ---:> rabots.yut Prep ---> v

s

J\ N l,{ 1~1 N.2...

tri bratS. r8.botayu t Pred

three brothers work in this in this three brothers ~~ork

117

ministry ministry

Gra.mma.r:

S ---) NP Ccipula. AP NP---> N AP---) Adj

NP

N

chelovek Sub

LT: In8.n AT: the m8.n

s

t f ' i :

,~ ~

. , ., . vyglY8~del

Predic8.te looked looked

FIG- 13

N ---) chelovek Copula---> vyglyadel Adj ---) usta.lym

,. l:"'- ,;" 1.;

llstalym

tired tired

118

Grallllnar:

S ---) ·NP yp NP---> N NP---> Pro NP---> APN VP---) y:JNP VP---> V NP AP---> Adj

N

professor Sub

VP

y

poprosil Pred

LT: professor asked AT:the professor asked

FIG-

N N Pro Adj V V

ns.s Obj

us to us to

119

14

---) professor ---) tetradi ---> ns.s ---> novye ---> poprosil ---) prinesti

prinesti I Obj

bring bring

Gr3.JnJnS.T:

LT: AT:

S ---> NP llP NP---> ~Jt.cl;)Vt...."N NP---) Pro . VP---) NP V

s

.' NP

I P.Jt,o p-vv N

I . \>fU1 I

G8.l~na. tet>Y8. Sub Obj

Ga.lina you Galin3. loves

FIG- 15

PKol~N Pro V

" I

lyubit Pred

loves you

120

---) Gs.lins. ---) teby8. ---) lyubit

LT: AT:

FIG-16

Gr8.mlv,u:

S ---> NP VP Proper Nt ---> Ivan NP ---> NP 1 Conj NP2 Proper N2 ---) Sergei NP1---) Proper Nl N NP Z---) Proper NZ Adj NP ---) AP N A 1.1 X VP ---) AUx V NP V AP ---) Adj Conj

s

T' Co,nj

r~op,'Vt. NIt r]l..O ~ N2.

I . IV8.n Serge~

Subject Ivan and Sergei are Iva.n and Sergei are

pytayutsya res hit , Predic8.te

trying to solve the trying to solve the

--.--~--~

---) zadachi ---) trlldneishie ---) pytS.YlltSya ---) reshit' ---) i

-NP

~ AP ',N I .

A~' ,J

I trudneishie z8.d8.chi

Attr Obj most difficult problems most difficult problems

FIG- 17

Grammar:

S ---) NP VP Proper N ---) Igr NP ---) Proper N N ---) armiYll NP ---) N V ---) postllpil VP ---) V AdvP PP V ---> sluzhit' PP ---) Prep NP Prep ---) v AdvP ---) V

s

AdliP P l"t.-o p -e J"t, N J pp

/\ I fJn.ep t'l p

I ~ Igr postupil sluzhit' v 8.rmiyu

Sub Pred AM (of purpose) AM (of pls.ce) LT: Igor came to serve in S.rlTly AT: Igor came to serve· in the army

122

FIG- 18

Gr8.muu~.r ;

LT: AT;

S ---) 1'1P VP N ---) knigi NP ---) N AP N ---) stole NP ---) 1'1 Adj ---) Vlad imir8. VP ---) v PP Adj ---) pis , mennom PP ---) Prep NP V ---) lezh8.t AP ---) Adj Prep ---) ns.

(Note: Since Russi8.n does not follow the'criterion of inte:Tupta.bility', the word "Vladim.ir8." (genitive of Vladimir) with inflection suffix's. has been tak$n as a single lexical unit)

S'

N AP ~ pp

AL ~.~ ..

p.J1.e f S\. P

I ~~. , Arj ~ knigi Vl8.d imir8. lezh8,t na pis'mennom stole

Sub Attr Pred Attr AM books Vl8.d imir' s 8.re lying on writing t8.ble Vladimir's books are hring on the writing t8.ble

123

Grammar:

S ---)

NP ---}

NP ---)

NP ---)

VP ---)

PP ---)

AP ---)

nl8,l' ohik Sub

LT: boy AT:the boy

FIG- 19

NP VP N ---)

N PP N ---)

N N ---)

A~ Adj ---)

v PP v ---)

Prep NP Prep ---)

Adj

s

/\ p/(e.~ :Np

a ________

AP N \ ,

AdJ

I v mekhovoi

Attr (without in fur

shapke agreement)

in fur

124

ca.p ca.p

ma.l' ohik sha.pke sa.du mekhovoi gulyal v

gulya.l Pred

strolled strolled

v sadll AM

in parl in the par

Grammar:

LT: AT:

S ---) NP VP NP ---) N PP NP ---) N VP --'-) V NP PP ---) Prep NP AP ---) Ad.j

NP

~ N pp

otets Sub

f8.ther fa.tiler

/\ s

with 8.nd

NfP

sy-nom Sub

son son

s

FIG- 20

N ---)

N ---)

N ---)

Adj ---)

V ---)

Prep ---)

'-.1.

rs.zr8.b8.t YV8.1 i Pred.

worked out worked out

125

otets synom. plany budushchie razra.ba.tyval i s

Nfl ~~.

AP ~ A~j I

\ . budushch1e plany Attr Obj

future future

pl8.ns plans

· FIG- 21

Gra.mma.r:

S --_.) NP VP N ---) syn NP ---) AP N Adj ---) ego VP ---} AdvP V AdvP Adv ---) khorosho AP ---) Adj Adv ---> po-rl.lsskt AdvP ---:> Adv V ---) govorit

"'-iP

AP N AdvP ''-1 J I

Aot' AcJ.v Aqv J l J '/ govorit ego syn khorosho po-russki

Attr Sub AM Pred AM LT: his son well speaks Rl.lssis.n AT: his son spes,ks Russis.n well

126

FIG- 22

Gramma.r:

S ---) NP VP N ---) NP ---) APN N ---) VP ---) V NP PP N ---) PP ---) Prep NP Adj ---) AP ---) Adj AP Adj -_._)

AP ---) Adj Adj ---)

Adj ---)

V ---)

Prep ---)

s

Ap J0p y

I

LT;' :( '{ollng scientists got the highest iiworc for their nel'l invention In: the ,(ollng scienti!.',ts got the hiqhe~t award far their ne .. inventirHl

127

l.1chenye na.gra.dl.l otknytie e'ti molodye vySOC ha.ishIlYl.1 novoe po h.lf.~hi 1 i za.

Gr8.mm8.r:

LT: AT:

S NP AP AP

dOll

Sub house my

~, ",'"

---) NP copula AP N --..,> k!qm ---> NAP ---> Adj AP ---> Adj

my friend's

Adj ---> Iloego Adj ---} drugs. Adj ---) bol'shoi CopuI8.---> ys

friend's house is

f bol' shoi Pred

big big

128

Gr8.ltI.rnar:

S NP NP AP

---) NP Copula NP ---) Proper N ---) AP N ---) Adj

FIG- 24

Proper N N Adj Copula

---)

---)

---)

---)

Ivs.n 8.rtist ta18.nt 1 ivyi cp

(Note: Copula and the link verb are synonymous in our context). .

LT: Ivan AT: IV8.n

Gramm8.r:

S NP NP

S .

is 8.

---) NP VP ---) Pro ---) AP N

~ A.P ____

A10U N

talant~ivYi ~rtist Attr (of the Pred) Pred

talented ts.lented

FIG- 248.)

N Pro Adj

artist s.rt ist

---) komnate ---) nikto ---) e'toi

VP ---) p8.rticle V PP V ---) sps.l PP ---) Pred NP Prep ---) v AP ---) Adj Ps.rt ic le-) ne

s-

Vp

r nikto ne spal

.R·'P ----~.~ Pi1e1'- ~p \ ~p.,..... __ ~'

, t A-~J k)' t e .01. omn.a e v

LT: nobody not slept in this room AT: nobody slept in this room

129

FIG- 25

Gramm.ar:

S ---) NP VP N ---) studen tl3.m NP ---) Pro N ---> knigi NP ---) AP N N ---) tsentre VP ---) V MP NP PP Pro ---) oni PP ---) Prep NP Adj ---) na.shim AP ---) Adj Adj ---) interesnye

Adj ---) klll'turnonl V ..:.--) poda.rili Prep ---) "

s

. NP

v

Px-o NP

ni pods.rili ns.shim studentaln interesnye knigi "

A0~ I_I' I.

AOJ I .' I k'ul'tlJ.rnom tsentre

Sub Pred Attr I ObjAttr Obj Attr AM

LT:they preserited our studenta interesting books in Cultura.l centre AT:they presented our students interesting books in the Cultural centre

130 .

FIG- 26

Granrm.8.r:

LT: AT:

S ---) NP VP Numeral--) dve MP --~) Nu.meral AP N N ---) knigi MP ---) N N ---) stole VP ---) V PP Adj ---) malen'kie PP ---) Prep NP V ---) lezhat AP ---) Adj Prep ---) na

(Note: Here the term Numeral has been used both as ~ funct ional 18.be 1 8.nd 8.S a c 18.s5 l8.be 1 )

s

NP

pp N u Jrl, IP N

MJ P,:e..p ~p N

dve malen'kie knigi lezhs.t I na stole Numeral Attr Su.b Pred AM

two Slt18.11 books are lying on ta.ble two sma.ll books 8.re lying on the' t8.ble

131

Gra,mmar:

LT: AT:

S NP NP NP VP PP AP AP

NP

lilY Sub

---)

---)

---)

---)

---)

---)

---)

---)

s

NP VP Pro PP Proper AP N V NP Prep NP Adj AP Adj

~o!'e with Victor Vietor 3,nd I

N

FIG- 27

Proper N-> Viktorom N ---) slov8,r ,

Pro ---) my Adj ---> novyi V ---) matematicheskii Prep ---) c· .;>

NP

Ap

~ Ac!- Ap

U I I AdJ' t 'I' , t ~ t' h k" 1 ' SOS.8Vl 1 nOVY1 ma.ema.1C eS~ll s oval'

Pred Attr Obj compiled new mathematical dictionary compiled a new mathematical dictionary

132

FIG- 28

Grammar:

S ---) NP VP NP ---) Pro N NP ---) AP N VP ---) AUX V NP PP PP ---> Prep NP AP ---) Adj Aft AP ---) Ad,j

s

i'\p

r T

Ja khochu poseti{ interesnye Sub Predicate Attr

LT: I want to visit interesting AT: I want to visit interesting

N --:--) u)esta N ---) gorode Pro ---) 18. Adj ---) interesnye Adj ---) e'tom Adj ---) istotichskom Aux ---) khochu V ---) poset ie Prep ---) V

N

AP ~

A~~ I ~

't: . . h' k d mesta v e ~om 1stor1C es om goro .e Obj Attribute AM

places in this historical city places in this historical city

133

We have listed above a few illustrative examples of parse trees

Here the following points may be noted:

l)For the purpose of simplification we have not

reflected punctuation marks in the parse trees.

However. this could be done as follows: S-)'Y\ ~ ,

S/~ ~ C' s - 1Y\C\.J' -::r s ..r1'Y1o.i,.. pL1nG ,) ~. f il'\o.i-Pt.lll c . J

NP vp

Pr 'V~ Jt..O NP

I Oil1 ,.( ':tMt N .

\.M 1<'Y\o\~ H~ '~ JtRCAcli"j CI. booK

2) In Russian. numerals, in a way. are attributes~

howeve~ in the present work they (like determiners in

English) ha.ve been used both as a functiona.l la.bel

and 8.S a class label. in

determiners ("members of a. subc l8.ss of

English

English

adjectival words that limit the nouns they modify in

a special way and that usually are placed before

descriptive adjectives") include a variety of words.

for instance, pre-articles (several of. many of. both

of). articles (def and nondef-thej

demonstrative pronouns (this, these ... ),

sOll'le) • b numers "

(cardinal, ordinal one, two, three ...• first.

second .... ). In our present3.t ion, we ha.ve ta.ken

pr'onomia.l adj ect ives, demonstrat ive pronouns, ord inal

numbers in the ca.tegory of a.djectives as they 8.re

clearly marked as attributes (normally adjectives).

Here it lIl8:Y be added th8.t the term "determiner" ha.s

134

only "'3.rticles" (a term used for determiners in

tra.d it ion'3.l English grammar) as the later is

generally restricted to the definitive article (the)

and indefinite a.rticle (a).

3) In schemes (parse-trees) and in earlierexpla.nations

also we have indicated preposition a.s constituent

e lemen t of '3. sentence p'3.rt. subj ect) under the "hes.d"

word. For insts.nce:

On zhivet yMoskye.

AM

(He lives in Moscow).

Ons. priekhs.la ~'detmi.

I Obj

(She ca.me with children)

4) Earlier, we discussed the structure of a Russian

sentence in terms of sentence parts (cf. Subject with

Attribute + Predica.te + Object with Attribute +

Adverbia.l Modifier). However, after the realisation

of different sentence parts by

speech (npun. pronoun ..... ) the

Russian sentence may appear as:

vs.rious ps.rts

structure of

of

8.

Adjective + Noun + Verb + Adjective + Noun +

Preposition + Noun

135

sentences (small and large) could be represented in

terms of the parts of speech. In fact, the above

structure of a Russian sentence is a combination of

"groups" (c8.11ed "phrases") (cf. Noun phr8.se/ Subject

Noun phrase + Verb phrase + Noun phrase/ Object Noun

phrase + I?repos it ion8.1 phr8.se> ie. NI? + VI? + N P +

PP). It implies that the identification of these

phrases is actually the identification of the

structure of a sentence. In other words, the

identific8.tion of the structure of a sentence

(whether it is a permissible structure or not, ie.

whether it is a sentence or not) based on different

phrase definitions implies identification of the

constituent phrases itself.

5) The homonymous word forms (homographs) , for

instance> "Ivan8." genit ive singul8.r of the proper

noun "Ivan") and "Iv8.na" (accusative singular of the

proper noun "Iv8.n"), h8.ve been kept in the

c8.tegor ies of 8.dject ives and nouns respect ive ly 8.S

the former denotes 8.n attribute and the later 8.ctS 8.S

an object. The following comparison of two Russian

sentence and their 8.dequ8.te tr8.nslation Jl'l8.y well

illustrate this point.

Knigi Ivana lezhat na stole (Attr/Adj)

(Ivan's books are lying on the table)

Ya videl Ivana v biblioteke (Object/Noun)

(1 saw Ivan in the library)

.136

Since we sore not representing os_se distinctions in PROLOG>

the above approach has helped in resolving the problems of

homogr8_phs. We shs_ll now expls_in the process of mechs_nics_1

tr8_nsls_tion by ts_king s_ gramms_r for simple Russian.

Russian-English Kechanioal Translation (using PROLOG)

Often in trans18.tion it is necessary to underst8.nd

what is being said before a proper translation oan be made.

Since a sentence in a language is much more than just an

arbitary sequence of words> a crude word-for-word approach

to translation> obviously cannot be acceptable. To m8.ke 8.ny

hes.dws.y, we hS.ve got to be s.ble to s.ns.lyse the structure of

8. senten'ce to parse it. To do ,this we will first

require s. simple Russis.n grs.mma.r to be defined.

A gr's_mmar for l8.ngus.ge such 8.S Russi8.n is a set of

rules for specifying what sequences of words are acceptable

8.S sentences of th8.t language. It specifies how the words

must group together into phrase and what ordering of these

phrs.ses 8.re 8.llowed. Given s. grs.mm8.r for a langu8.ge (here

Russ i8.n) we can look 8.t s.ny sequence of words a.nd see·

whether it meets the criteria for being acceptable sentence.

Let us define the geners.l structure of s. subset of Russis.n

simple sentence with the help of the following context-free

gr8.mm8.r:

sentence noun _phrs.se nOl.ln_phrs.se noun_phr8.se verb_phrase

---) noun_phrase, verb_phrase. ---) numers.l, s.dject iva> noun.· ---> adjective, noun. ---) proper_noun. ---> verb> prep_phrase.

137

verb_phrase prep _phrs.se prep_phrase

numeral numeral noun noun noun name name verb verb verb verb 8.dj 8.dj 8.dj preposition

.---) verb. ---> preposition> noun-phrase. ---) noun_phrase.

---) dve. ---) tri. ---> knigi. ---> ruchki. ---> stole. ---) IV8.na. ---) VIs.d imir. ---) lezhat. ---) stoys.t. ---> spit. ---> napis8.1. ---) Ins.len' kie. ---> pis'meonom. ---) derevyannom. ---> 08.

("t'"'o" ("three" ( "books" ( "pens" ("table" (-(-("are lying" ("8.re sts.nding" ("sleeps" ("wrote" ( "small" ("writing" ("wooden" (.. II . on

The grammar consists of a set of rules, here shown on

to a line. Each rule specifies a form that a certain kind

of phra.se can tS.ke. The first rule says that a sentence

consists of a phrase called a noun_phrase (a sentence can

take the form: a noun_phra.se.1followed by s. verb_phr8.se). The

second, third, fourth, fifth, sixth, a.nd seventh rules of

the grammar tell us what constitute grammatical forms of for

other phrases. The other rules in the grs.lUlUs.r SS.y how some

phr8.se can be ms.de up in terms of s.ctus.l words, rS.ther in

terms of slls.ller phrases. The things on the right hand side

nS.me 8.ctu8.1 words of. the 18.ngIl8.ge ( Russ i8.n ) , so that the

rule

numeral ---) dve.

c8.n be res.d S.S!

A numeral can take the form: the word dve. H8.ving

explained the gr8.mm.8.r, we can begin to see what sequences of

words are actually grammatical sentences according to it,

138

(sinoe this is a small grammar. it ~ill aocept sentenoes

formed out of fifteen different words). If we wish to

investigate whether a given sentence of words is aotually a

sentenoe s.ocordingly to these oriteria we, need to s.pply the

first rule to ask.

does the sequence deoompose into two phrase, such ths.t the first is 8.n 8.ccepts.ble noun_phrase and the second is a valid verb_phr~se?

Then in order to test whether the first phrase is a

noun_phrase, we need to s.pply the second rule 8.sking.

does it decompose into a numeral followed by s.n 8.djective a.nd then followed by a. noun?

and so on. At the end, if we.succeed~ ~e will have looked

at 8.11 the phrases a.nd sub-phrases of the sentence. 8.S

speoified by the grammar, and will have established a

structure like, for instanoe: (see fig. on next page)

This dia.grs.m (s. pa.rse tree for the sentence) shows

the phrase structure of the sentence.

We h~.ve seen how hs.ving a. grs.mms.r for Russia.n me~ms

th8.t we can construct pS.rse tree to show the structure of

Russian sentences. The prQblem of constructing a. pa.rse

tree for a. sentence, given 8. gra.mm.a.r, is called, as

m~ntioned earlier, the parsing problem. A computer program

that constructs parse trees for sentence of a language is

cs.lIed a. pa.rser.

Now let us see how a Russian sentence is parsed

aocording to the a.bove-mentioned grammar. Since 8. Prolog

progra.m (pa.rser), which pa.rse simple Russian sentences

139

( tWO

noUn:" phR..a.se

L\~J ~d:tve

p;l",en~orn

automatioallY. involves testing to see if something is a

sentenoe) let us define a predioate sentenoe. -~ x -" ... "----<; .. __ 0_- ..-.~... __ ;:,_-..,_.~" ''.~ ~.-",!-,_.yr ....... __ _

- -~/ .... This predicate will

succeed . if a sentence can be parsed properly. and fails if

it cannot. For example, if this predicate is applied to the

sentence: dve malen'kie knigi lezhatns. e'tom pis'mennom

stole should fail. since this sentence oannot be parsed

according to our grammar.

Now consider the problem of checking that a list of

words ( a sentence is held as a Prolog list) is a valid

sentence. A list of words is a valid sentence if:

1) the list has a valid noun phrase at the front;

2) what is left after ~he noun phrase is a valid verb

phrase.

Therefore, given the list of words:

r----lr----------~~------1--------~-----I-----------T-------~ ! dve; IDa.len' kie' knigi I lezh8.t I na. pis' mennom i stole I ~ ____ ~ ___________ ~ _______ L ________ ~ ____ L ___________ L __ _____ ~

The first question which need to be asked is:

Does this list have a valid noun phrase at the front?

If so. remove the noun phrase from the list.

The answer to this question will turn out to be yes

(a.coord ing to our gram!l18.r) > removing t.he noun phrase from

the front leaving the list:

~------------------------------------------------~ I lezhat! na! pis'm.ennom; stole : 'L ________ .J ________ .l.. _____________ ._.l.. ________________ .J

The question to be asked at this stage is:

140

Does this list ha.ve va.lid verb phras a.t the front?

If so, remove the verb phrase from the list.

Again the answer to this turns out to be yes, removing the

verb phrase from the list leaves just the empty, list.

So the strategy for parsing a list of words may be

described as follows:

1) Ta.ke the input list.

2) Identify each of the exp.ected eomponents or phra.ses.

one a.t 8. time.

3) Each time a phrase is identified, remove it from the

front of the list.

4) This remainder is used as the list for identifying

the next component.

5) At the very end~ there will be a remainder list

(possibly empty); this will be returned by the

process as left over.

Parsing a sentence is just a special case of parsing

any type of phrase or structure. Therefore. for each sort

of structure

structure) we

(a. sentence> a.

will have a

noun phrase. or any

sep8.ra.te pred ica.te.

other

Ea.ch

predicate will take an input list. and will specify the

conditions under which there is an occurence of that

structure at the front of the list. If the predicate

succeeds, it will return the left over as an input list

after the structure has been stripped off at the front. If

the input list does not meet the conditions. the predicate

shOll ld fa.i L

141

Based on the a.bove understs.nding s. parser comprises

a set of predicate defining validity requirement of

different structures a.nd voc8.bularly definitions.

Let us first consider the predicate which defines

~hat a valid sentence is. As with any other predicate which

pS.rses a structure, there will be an input list and output

(remainder) list:

Sentence ([--~input list---),[---output list---):-

In completing this predicate, we will assume the existence

of whatever other predicates are required. The 8.Ctu3.l

predics.te is:

sentence (In, Out):- noun_phrase (In, Temp), verb_phrase

(Tem.p, Out).

This states that the input list In has a valid sentence at

the front (returing the left-over list of words called Out)

if in starts with a noun phrase (leaving an intermediate, or

temporary list Temp) and Temp starts with a verb phrase

(le3.ving the re\ll8.inder list Out). The division of the list

c8.n be shown dis.gr8.ms.tics.lly as follows:

r-------------------~-T~------------------T-----------------e ~--noun phrase--- t--verb phrase--- I---Out--- • L _______________ --------------------------~-----------_____ J L-----------input list In-------------------------------J

L--------------Temp-------------------J L---remainder---- J

Assuming that the predic8.tes noun_phrase s.nd verb_phrase

could now ask

? ---sentence ([dve, malen'kie, knigi, lezhat, ns., pis'mennom, stole), [ )).

142

This would return the answer yes. Here the query specifies

that the remainder list must be empty.

Now consider the defination of noun_phrase:

noun_phrase ([---input list---],(---output list---]).

According to our grammar~ there are three possible forms a

v8.lid noun phra.se C8.n t8.k.e, so we will h8.ve three sep8.r8.te

Prolog clauses (i.e statements of facts in Prolog), each of

which will define one valid form of a noun_phrase:

1st case:

noun _phr8.se--- > numer8.1. adject i ve, noun

the detailed format of the input list of words which has a

noun phrase of this sort at the front (and whioh has a

lett-over list called Out) is follows:

r-----------------------------------------------------------1 I numers.l. I 8.dject hre i noun !----Out---- I

~------------L------------~-----------~--------------- _____ ~ L-----------------------------input-------------------_____ J

!..~rema.inder------------J

The predicate whioh check.s that the input list oonforms to

this patterns is:

«( Num, Adj, Nnl Out], Ollt):­numeral (Num), 8.djeotive(Adj) , noun (Nn).

(Here is m8.y be noted that if number and gender features are

to be included, then the above predicate will look like:

noun_phrase numer8.l 8.djective noun

([Num, (Num, (Adj, (Nn,

Adj, N n : Out] , N ,G), N ,G), N ,G).

Out, N ,G):-

However, 8.S mentioned earlier, in the present work we are

143

not considering feature set representation in this form).

The B.bove predicB.te tB.kes B.n input list of words and

decomposes it; the first word is called Num, the second Adj,

the third Nn, and the remaining list of words is called

Out. It may be noted that it is by placing this same

remainder list in the output position we strip off the noun

phrase from the input list. Next. the first three words

are tested to check that they are of the right class. If

the decomposition and the checks all succeed, then the

pred icate suoceeds. For instance, the query (GoB.1 in Turbo

Prolog)

? ---noun_phrase «(dve. malen'kie, knigi, stoyat), 011 t) .

would succeed, setting Out to the list (stoyat)> whereas the

query!

? ---noun _phr'J.se «(dve, ruB. len 'kie, interesnye, kn igi, stOY8.t], Out).

would fB.il B.t the third condition in

the third word in the list (referred to wi thin the"

predicate as Nn) is being tested by the predicate noun. The

condition:

noun (intersnye)

would fail, assuming that interesnye has been defined as an

adjective.

2nd case:

noun_phrase----> adjective, noun

The detailed format of an input list which has a noun

phrase of this second type at its front could be shown

diagrammatically as follows:

144

r - - -- - - - - - - - - -J- - - - - - - - - - - - - - - - r - - - - - - - - - - - - - - - - - - - - - ----: I adjective ! noun . I'---------Out-------- .: , L ____________ J ________________ L ______________________ -.

L---------------------input _________________________ J

L----remainder--------- J

A second definition of the predicate nounlphrase, (which

succeeds onlu if the input list has this ~tructure) is as

follows:

noun_phra.se adjective noun

3rd ca.se:

«(Adj, NnlOut], Out):­(Adj) , (Nn).

noun_phrase----> name

In this case, the input list must merely start with a

word which ha.s been defined to be a. na.me. Everything after

this ~ord in the list is the remainder. ~he predicate is

therefore:

noun_phrase «(NameIOut], Out):­name (N a.me ) ..

The following eX8.mple queries may well illustrate the

operation of noun_phrase:

(i) ? --noun_phrase «(ivan, spit], (spit]).

This succeeds; the fiest two definitions of noun __ phrase are

tried 8.nd f8.il. But the third cla.use ma.tches.

(ii) ? noun_phrase «(dve, ma.len'kie, knigi,lezhat, na,pis'mennom stole], Out).

This first cla.use defining noun_phra.se successfully parses

this input list, a.nd produces the answer:

Out = [ lezhat, na, pis'mennom,stole]

145

(iii) ? --noun_phrase {(na, pis'mennom) stole], Out].

( cf. Is the input list na,pis'mennom, stole a noun phrase?)

This will fail as none of the three cl~lses for noun_

phrase will match this input list.

Verb phrases

A va.lid verb phra.se can be defined by two a.lterna.tive

clauses for a 'predicate verb_phrase. This predicate should

succeed if the input list of words begins with,a valid verb

phrase, in which case the list of remaining words should be

returned as the output list:

verb_phrase{(---input list----], (---output list---]).

Again, let us consider each of the two cases of a

verb phr8.se a.s defined in the gr8.mma.r sepa.ra.te ly.

1st case:

verb_phra.se---) verb, prep_phra.se

For the input list of words to start with a verb phrase of

this pa.rticul8.r from) it must ha.ve the following structure:

r-----------r--------------------r---~----------~--~--4 1 b"' I h "0 t ". , ,I ver ..... --prep P ra.se--- I --- u--- ,.", 1

, J L~ ____ ~_~ __ ~~_~ ____________ ~~ ____ L ___ ~ ____ ~~ ____ ~~ ___ ~~

L~--------~-------input---------------------~-~---~--~~ ~ L_ ----Temp ---------- - - -- -- -------"- -"-';;"-- __ :;..I ."

Lremainder----------- J

An input list which has this structure is defined by

the predicate:

verb_phrase '((VlTemp), Out):-verb (V), prep_phrase (Temp, Out).

This states that an input list of words is a valid verb

phrase of this type if the first word is a. verb, a.nd if wha.t

comes after this verb (the list Temp) starts with a

preposition phrase. The left-over from pre~phrase is then

146

the left-over from the whole verb phrase.

2nd cS.se:

verb_phrase--> verb

The clause which defines this altenative succeeds if the

. first word is s. verb, and returns the input list of words

minus this first word:

verb_phrase «(VlOut], Out):­verb· (V).

For example, the querry:

?- verb_phrase ([lezhat, na, pis'mennom, stole], Out) .

The fist clause is selected, setting Out to the empty list.

Like wise, the querry

?- verb_phrase ( ( spit], ( ]).

will sllcceed.

Preposition phrase3

The grs.mms.r sts.tes ths.t there s.re two forms which 8.

valid preposition phrs.se C8.n tS.ke. We will consider es.ch

cS.se seps.ra.tely> s.nd define 8. cl8.use for es.ch one.

1st C8.se:

An input list of words which has this structure is defined

by the predicate:

prep_phrase([PreplTemp), Out):­. preposition (Prep),

noun_phrase (Temp,Out).

147

2nd case:

This is defined by:

prep_phrs.se (In,Out):­noun_phrase(In, Out).

For example, the following query will succeed:

?- prep_phrase ([na,pis'merinom, stole),( ]).

The first clause for prep_phrase will be selected to parse

this input list of words.

Gathering together all the above-mentioned predicates,

plus the vocs.buls.ry definitions, gives us s. ps.rsing progrs.m

whioh checks for proper structure of simple Russian

sentences.

Having defined the parsing program, let us now trace

the evalution of the querry (i.e evaluation of the goal):

Sentence ([ivan, napisal, dve, malen'kie knigi),Out)? l\llon_phrs.se{[ivs.n, ns.piss.l, dve, m.alen'kie, knigiLTem~?

{Try first clause for noun_phrase} numers.l (ivs.n)? - fs.ils

{Try second clause for noun_phrase} s.djective (ivs.n)? -fails

{Try final clause for noun_phrase} ns.me (i vs.n )? -suoceeds

-succeeds, setting Temp to (napisal, dve, malen'kie, knigi) verb_phrase [ (napisal, dve, malen'kie, knigi),Out)?

{Th~,tb~first clause for verb_phrase:} {initially sets } {V = napisal,Temp=[dve,malen'kie,knigi]} verb (ns.pisal)? - succeeds prep_phrase ([dve,malen'kie,knigi),Out)?

{Try the first clause for prep_phrase:} preposition (dve)? -fails

148

{Try the second cls.use for prep-phrs.se}

noun_phrase «(dve,malen'kie, knigi], Out)?

{Try the first clause for} {noun_phrase: } {initially sets Out=(] }

numeral (dve)? (of. Is dve a numeral? -succeeds adjective (malen'kie)?

-succeeds noun (knigi)? -suoceeds

-succeeds, ~eturing output list ( ] -suooeeds, returing output list ( ]

-succeeds, returing output list [ ]

As it is known the translation of a sentence (say

from Russian to English) has to be dorie by building the

English structure tree/ ps.rse tree from the Russis.n text

version of the sentence ~hich is recognised (parsed). For

insts.nce, the Russis.n sentence, in the text form:

dve malen'kie knigi lezhat na pis'mennom stole

this would be 'translated'

representation which we will

respresented by the following tree:

s.ente.t"Ice..

two bDoles

149

(tra.nsfered) into

cs.ll structured

011

a

tree

Now we will look at the task of how to build an

'English structure tree from 3. Russia.n sentence, by extending

ollr par'sar progr3.m so that it bu i Ids Q.< d e.scr ipt ion of the

structure tree as the various parts of the sentence are

recognised by the parser program.

Building ~ Structure ~.:fI.Q.m. Russian

We can already recognise the different components of

3. Russian sentence using our p8.rser prc1gr3.m. No-w a.ll we

have to do is to build a Prolog structure for every part

-which is recognised.

A component of a structure tree can be represented in

Prolog as a structured object. Each different kind of

structure will be represented ·by its own kind of object.

The components of this object may themselves be structured

objects. In structure tree:

which represents a grammatical structure or phrase of a

p3.r t iculs.r kind, a.nd with

represented by the Prolog object:

kind (C 1, C2 , C3 ,---, Cn )

150

several components, is

First W""'e will work through e~.ch kind of structure > ~.nd

outline the kind of object used to represent each case.

Then the problem of builiding these structures trees

automatically in Prolog wil be tackled. We will start with

the simplest kind of structure--a basic word.

Reprp.~enting B~sic Words

Each word will be held as a simple structure. which

specifies the type of 'word it is and wh3.t the wo-rd itself

i~. Each kind' of word is represented in Prolog by it own

kind of structured object .

.( c f ' 11 a Me. l.i v 0. l'\) 1 Representing Higher Leyel Structure

The object representations which are built for

individual words as shown above must be put together to

build higher level object descriptions of phr~.ses a.nd

structures within a sentence. Let us consider each kind of

structure in turn.

tlQJ.ID. Phrases

When we parse a Russian noun phrase. we need to build

a. t'ree structure which will hold the components of the noun

phrase. Since there are three different cases of a noun

phrase. we will consider each case separately.

1st case:

noun~phra.se--> numers.l. s.dject ive. noun

A noun phrase of this type will be represented by

A Prolog objects which represents this would be: noun.c.:Phrase (NuemraL Adj ect i ve, Noun)

2nd ca.se:

"noun2phrs.se-- > a.dj eet i ve > noun

A representation of this type of noun phrase appears to have

two components. However. to avoid oonfusion between a noun=

phrase object with three components ~.nd one "\:o,lith two, we

will regard this second case as though the numeral is

missing, and fill up the vS.cant numers.l position in the

object with a dummy component. The res~lt is a standard

representation for noun phrase which normally (until we have

a case of unbounded de~endencies) have three components. We

c~.ll this dummy component wh~.tever we want, say nil. This

gives a structure tree for the second type of noun phrase as

follows:

{ mOUn J 3rd case:

noun6phrase--> name

There is no need to build a higher level tree which has only

one component, we will just use tree which W8.S built for

na.me.

Yfu:.:.b. Phrases

1st case:

152

The structure tree for this form of phrase is:

G~ C ~ C-=_=~

{ v.erLh J {p~p- ph~qse J which is r~pr~s~nt~d in Prolog by th~ obj~ct

verb_phrase (Verb, Prep~phrase)

2nd case:

verbOphrase--> verb

Here we wil~ just use the tree which was built for the verb.

P~eposition phrases

1st case:

prep_phrase--> preposition, noun_phrase

The structure tree for this type of phrase is: p..u.p_ phxa....s-e..

This is represented in Prolog by the object:

2nd case:

prep_phrase--> noun_phrase

In this case) it is sufficient to return the tree for noun_

phrase.

Sentences

The tree for a complete sentence has two components:

~ c-. ----"""-5 - ~ ~ ~ ",,01A"I')_ ph'l.IAS-€} <- VeAh_ph/t..Ct.S-e}

which C8.n be held in Prolog 8.S the object:

sentence (Noun_phrase> Verb_phr:,ase)

153

Building ~ Structure ~ E.r.!m. Russian (Automatically

To trans18.te from Russian into English 8.S mentioned

e8.rlier, will be to tr8.ns18.te from Russi8.n text into

Structured English: in other words, to input a list of

Russi8.nwords, 8.nd produce 8. represent8.t ion in Structured

English. For this, we extend our Russian parser so that it

not on ly 8.n8.lyses the structure of a sentence in R11ssi8.n,

but also builds an English structure tree for the sentence.

Thus, our extended Russian parser, which produces 8.n English

structure tree from a lsit of Russian words, must contain

English words, r8.ther th8.n Russi8.n words. For inst8.nee, the

Russian noun_phrase:

dye malen'kie knigi

will be represented by the structure tree

(1'\e~'YI_ r h fC-Q.se

'-! 'Y\OUYV'

-two SYYlo,J...L bootes

Thus, it ia in thiaprocess ~ building ~ structured English

representation ~ ~ Russian sentence where ~ ~

translation ~ ~.

Building Structures ~ Basic Russian Words

The vocabulary-defining primitives, i.e. basic words

(r_noun, r_verb, 8.nd so on) have now to be modified to

return a tree for each basic word. Again, the clause which

builds the tree for the word has to be separated out from

the actual definition of the word itself in the vocabulary,

since the vocabulary proper does not define the structure

154

tree for each word. So, between the vocabulary and the

parser we interpose a set of simple predicates which build

the structure tree for each Rllssis.n word. Ags.in, we rens.me

the vocabulary predicates, from r_noun, r_verb, and so on~

to be noun_r, verb_r, etc. These old names, such as r_noun,

s.re used for the intermedis.te tree-building predics.tes. The

vocabulary itself now becomes

numers.l_r numeral_r noun-,r noun_r noun_r name_r nS.me_r verb_r verb_r verb_r verb_r adjective_r 8.dj ect i ve_r preposition_r

(dve) . (tri). (knigi) . (ruchki). (stole). (ivan) . (vladimir), (lezhat) . (stoyat) . (spit) . (napiss.l) . (malen 'kie). (pis'mennom). (na) .

Now let us consider what English structure trees have to be

built for each type of basic word; For instance, for knigi,

which satisfies:

nOl.ln_r (knigi)

The corresponding tree represented as a Prolog object should

be: noun (books)

To construct this object, we will obvioulsy need to know the

English word which corresponds to each Russian word. As

mentioned earlier, this could be specified using the

predicate me~ms:

means (knigi,books). means (malen'kie,small). means (pis'mennom, writing).

155

However, to choose the right English word to store in the

tree, it is desir8.ble to include the English transls.tion of

a Russian word in the vocabulary. Including this as an

extra component gives the revised vocabulary:

numeral_r nl.lmer8.l r noun_r noun_r noun_r name_r name_r verb_r T/erb_r verb_r verb_r adjective_r 8.djective_r preposition_r

(dve> two) . (tri,three). « kn igi, books) . (rl.lchki > pens). <. stole, table). ( i vs.n , i V8.n ) . (vlad imir, vlad imir). ( lezhat ,Qni hi i n9), ( s t ?ya t > i:l..J.:~:_SJ9~:'(lct i1"j) . (splt,sleeps). (ns.pisal, wrote). (malen'kie,small). (pis'mennom,writing). (na,on).

In general, the English structure tree for any Russian word

is now defined by a predicate:

r_noun (Russian word, structure tree)

which can be defined in full by:

r_noun (R_ word, noun (E_ Word»:­noun_ r (R_ word, E_ word).

For insts.nce:

?-r_noun (knigi, Tree).

would produce the reponse:

Tree:noun (books)

The predicates for

defined simi 18.r ly:

r_ numeral

r name

r verb

r - adjective

other types of Russian word can be

(R_word, numeral (E_word»:­numeral _r (R_word, E_word).

(R_word, name (E_word»:-name _ r (R_word, E_word).

(R_word, verb (E_word»:-verb _ r (R_word, E_word).

(R_word,adjective(E_word»:­adjective~r (R~word.E_word).

156

r _ preposition (R_word,preposition(E_word»:­preposition_r(R_word,E_word).

(Here it may be noted that E_word, in a way, isa translated word) ,

Building Trees ~ Bussian Phrases

Now let us consider the higher level trees which are

.built using the basic trees for individual Russian words.

For e8.oh 018.use in the Hussi8.n pS.rser, we 8.dd 8.n extra ps.rs.

meter, whioh is structured English tree to be built.

Consider eaoh cls~se in turn.

t:lmm Phrases

1st oS.se:

Extending the ear;ier clause for parsing Hussian

noun phrases of this form gives the following olause:

r_noun-phrase «(Num,Adj,NnIOut), Out> noun_phrase-(Num_tree, Adj_tree, Nn_tree»)

r _ numers.l (Num, Num_tree), r adjective(Adj, Adj_tree), r noun (Nn, Nn_tree).

It should be obvious from this that the tree object returned

for the input list: (dve, malen'kie, knigi)

is: noun_phrase (numeral(two),8.djcetive (small),n'oun (books»

2nd oase:

To make sure that both types of noun-phrase object have

three components, the 'missing' numers.l should h8.ve its

position failed with the dummy object nil:

r_noun_phrase «(nil, Adjeotive, NnlOut), Out,noun_phrs.se(nil, Adj_tree, Nn_tree»):-

157

~ ..

r_adjective (Adj, Adj_tree), r_noun (Nn, Nn-tree).

3rd case:

The tree for this form of noun phrase is just the tree for

the name:

r_noun_phrase «(Name\Out], Out,Tree):­r_name (Name) Tree).

~ Phrases

1st case:

The previous predicate must now have an extra third

argument, which is the tree for a verb phrase of this form:

r_verb_phrase ([VITemp],Out, verb_ phrase (V_tree, P_Tree»:- r_verb (V, V_tree), r _prep_phrase(Temp ,Out ,F _tree).

2nd case:

r-verb-phrase--) r_verb

The tree returned in this case is juctthe tree built for

the verb, called V_tree:

r-verb-phrase ([VIOut], Out) V_tree):­r_verb (V, V_tree).

Preposition Phrases

1st case:

The previous predicate, which had two arguments, now had a

third, which is the object built to represent the structure

tree for the preposition phrase:

158

r_prep_phrase([PrepITemp)~Out>prep_phrase(P-tree>N_tree)}:­r_preposition (Prep, P_tree)~ r_noun_phrase (Temp, Out) N_tree).

2nd case:

r _,~p_phrase--> r _noun_phrase

Here the tree for the preposition phrase is just the tree

for the noun phrase:

r_prep_phrase (In,Out, P_tree):­r_~oun_phrase (In, Out, P_tree).

Sentences

The last clause to be extended to build the tree

representation is that for processing a sentence. This

builds an object ~ith two components:th~ trees for the noun

phrase (N_tree) and

r_sentence r.:..noun_phrase r_ verb_ phrase

sverb phrase (V_tree):

(In, Out, sentence (N_tree, V_tree»:­(In, Temp, N_tree), (Temp, Out, V_tree).

Puting all these clause:3/ together, s.nd s.dding structure

trees for bs.sic Russis.n ~ords and the vocs.bu ls.ry (Russis.n-

English), gives us a progrs.Kl which parses Russian sentence

and builds the structure tree for it.

As an example of this program's operation, consider the

follo~ing querry:

?- r_sentence ([dve, malen'kie> knigi,lezhat, na, pis'mennom stole], [ ], Tree).

This querry will succeed, and will produce the

representation of the structure tree as a Prolog object Tree

s.s shown be low.

159

Tree = sentence (

Implementation

noun-phrase ( numers.l (two), adjective (small), noun (books»),

verb-phrs.se ( verb (are lying) pref.phrs.se (

preposition(on), noun-phrase (

adjective (writing), nil, nQun (table}»»)

New fifth generation programming languages,

inc lud ing Tu rbo Pro log, a.nd new imp 1 il1en ts.t ions 0 f these

ls.ngus.ges have al~ays been the stimuls.s for developing

exciting new applications in areas previously untouched by

electron ic comput ing ms.chines and computer techno logy. In

rercent yes.rs, Turbo Pro log has become 8.n imports.nt

instrument in implements.tions of Al technology in s.res.s such

as expert systems development and ns.tl1rs.l ls.nguage

processing.

MDTS has been imple~ented in Turbo Prolog (version

2.0) , We chose this progrs.mming la.nguage on account of

its outstanding features, such as closeness to natural

langus.ge, modl.lls.rity, one la.nglJ.8.ge for progrs.m a.nd da.ta.,

logics.l vs.r is.b les, computa.t ion of re ls.t ions,

understandability, lea.rnabil ity, inbuilt sea.reh strategy,

etc ..

MDTS programme mainly consists of five modules:

160

INPUT ,CONTROL, SYNTAC ,DICT a:nd OUTPUT. Module INPUT hS.ndles

the natura.l input sentences by tl':3.nsforming th~ sequence of

written words and punctuation marks into lists of words and

marks, and it checks whether every sentential component is

known.

Module CONTROL is the high level control structure of

all processes occuring during the translation process. It

calls the module SYNTAC for analysing the sentence~ deciding

upon its result to accept or to refuse it. In the case of

accepting it. it calls the modules DICT and OUTPUT to.

interpret it. and to perform the appropriate translation.

Module SYNTAC handles a CPG. It takes a list,

representing the natural language input sentence and

applies several knowledge sources to yield a logical

sturcture t h:3.t con t:3.ins all the inform:3.t ion for a. semant ic

interprets.t ion. It is able to extract from a set of

possible rea.d ings the a.ppropria.te res.ding. The grammar,

, being able to generate all and only the sentences of a sub­

set of Russia.n, recognises if the input sentences be long to

that sub-set of Rusian syntax patterns and establishes a

relation between the written sentence and its meaning.

Module DICT contains the language specific knowledge

a.bout the written input a.nd output sentences. 1. e. a

dictionary of lexical items to support 5L-TL rendition.

Modu le OUTPUT ha.nd les the forma.t ion of TL sentences.

161

Performance at ~ System (HOTS)

1) The system can handle a variety of syntactic structures

involving principal and secondary sentence parts

(Subject, Predicate, Object, Attribllte and Adverbis.l

Modifier) within the category of grammatically simple

sentences (see appendix III).

2) The system (on experiments.l bs.sis) cs.n h8.ndle s. few

types of compound (cf. S--> Sl ConjS2) and complex

sentences (see appendix IV)

3) The system can handle word order at the segment levels

dom ot tss.---house father's ---) father's house---

(cf. r_ Sub «(Word\ (Word 1\ [ ]]], (T word \ (T word r_ noun (Word,T word 1), r_adj (Word

word) .

---Khorosho govorit po-russki: ---well r _ Pred

r_adv r 8.dv

speaks Russi8.n --> spe8.ks Russis.n well ([Wordl(Word 1 : (Word 2 I ( ]]]], [T word

IT word 11 [T word 2\ ( ]]]]):-(Word, T word 1), r_ verb (W,ord 1, T word 2), (Word 2, T word).

4) The system C8.D ha.ndle tra.nsI8.tion8.1 tr8.nsforms.tions such

8.S lexica.l additions s.nd omissions.

(cf. s.ddition: (On) tals.ntlivyi artist (he) talented artist --) (he) is talented artist r Pred «(Wordl( Word 1\ ( ]]], (T word: (T word 1; (T

word 2\ ( ]]]]):-

r link_ verb (T word), r_ adj (Word,T word 1), r noun (Word 1, T word 2).

Omission (deletion):

(Nikto) ne spit (Nobody) not sleeps--) Nobody sleeps

r_Pred [Wordl(Word 11[]]], [T word 1'( ]]):­r_particle (Word), r_verb (Word 1, T word 1).

162

Here it m8.y be added that some regrouping may be required if the system is extended to . - 1nclude various other types of syntactic structures.

5) The system can partially resolve homographs by

identifying them as different word classes (in different

syntactic structures)

(cfl) uchenyi provodil interesnye e'ksperimenty Nelln + (Verb + Adj + ~lolln) scientist oonducted interesting ~xperiments.

2) llchenyi sovet prinY8.I pravil 'noe reshenie Adj + Noun + (Verb + Adj + NQun) . 8cademio oouncil took correct decisionF-- '1

6) The number agreement (on experimental basis) could~be

achieved by distinguishing two kinds of noun 'phrs.ses "and

two kinds of verb phrasee (cf.sentence --) singul~r

noun_ phr8.se, sigulB.r_ verb_ phrase; sentence--> pll.lr8.l_

noun_ phrase, p111rB.l _ verb _ phr8.se---). This type of

grammar works correctly but is quite redundant. As

mentioned earlier, (additional arguments could be used

to h8.ndle gra.mms.tic8.1 8.greement, however, in the

present work we 8.re restricting our.::....;~selves only to the

qontext-free grammar) 8.S per our initi8.l work pla.n.

7) The. system (supported by a large ds.tabs.se 8.nd a va.r iety

of syntactic structures) works effectively, however, in

the present form, it

i) can generate nonsentences. ii) cannot differentiate between the homonymDus word

forms belonging to the same gramma.tic8.l catego'ry/word cls.ss.

cr.l) professors pishut nauchnye stat'i (noun in the nomina.t ive p lurs.l)

Professors write research articles. 2) ya videl professors.

(noun in the iccusative singular)

163

.'.

I saw professor

Here it may be added that in many cases we have different

word forms for reflecting the above grammatical information.

For instance:

nominative plural studenty (stUdents) pisateli (writers) prepodavateli teeachers)

accusative singular studenta (student) pisatelya (writer) prepodavate lys. (teacher)

(As regards the homonymous word form 'professora' (noun in

the genitive singular) we could still differentiate by

including this word form in the grammatical category of

adject ives for the sS.me S.cts 8.S 8.n 8.ttribute. In case of

inanimate nouns, the homographs, generally, do not pose any

problems S.S their lexic8.l meanings 8.re the

same. For instance:

iii)

knigi (nominative plural) lezhat na stole (Books 8.re lying on ., " -"table)

ya videl interesnye knigi (acousative plural) (I S8.W interest ing books)

C8.nnot handle those typical s i t.US. t ions where

sentence ps.rts (ss.y Subjeot 8.nd I Obj) could

the

be

identified only on the bs.sis of grs.mms.t ios.l

s.greement. This point ms.y be illustrated

comps.ring the following two sentences having

same syntactic structure

1) t!~ ~_ Vikt<?!,,9~! poshli na rynok. \~uoJ (Sub) (plural-verb)

Victor ~ 1. went to the market.

2) Ya (Sub)

poshel ns. rynok (s inguls.r-verb)

L went to mS.rket Ri.t.h Victor.

by

the

Here' it InS.y be added tha.t the Russian preposition

"n8." in the present context (verb of mot jon +

preposition + noun in the accusative case) means

"to".

164