NAmlmAL LANGUAGE INFO~4ATION RET£IEV~ SYST~4 DI.&LOG 
L. 9o\]0, K. Kochut, A. Le§niewski I, T. Strzalkowski 2 
Warsaw University 
Institute of Informatics 
PKIN, pok.850 
00-901 Warszawa 
POLAND 
~BSTRAe~ 
Presented paper contains a descrip- 
tion of an experimental version of the 
natural language information retrieval 
system DIALOG. The system is destined 
for the use in the field of medicine. 
Its main purpose is to ensure access to 
information to phlsiclans in a conver- 
sational manner. The use of the system 
does not require ability of programming 
from its user. 
I.. Introduction 
The paper presents the state of 
elaboration of the natural language in- 
formation retrieval system DIALOG. Its 
aim is an automatic, conversational ex- 
traction of facts from a given text. 
Actually it is real medical text on 
gastroenterology, which was prepared by 
a team of specialists. The system has 
a modular structure. 
The first, and in fact very import- 
ant module is the language analysis mod- 
ule. Its task is to ensure the transi- 
tion of a medical text from its natural 
form, i.e. rentences formed by phys- 
icians, into a formal ~ogical notation. 
This logical notation, i.e. logical for- 
mulae, is rather universal and can be 
easy adapted to various deductive and 
knowledge representation methods. The 
program of the analyser was written with 
the use of the CATN /Cascaded ATN/ tech- 
nlque, where the syntactic and semantic 
components constitute separate cascades. 
In the deduction and knowledge rep- 
resentation module the weak second order 
language was used. The works by E.Konrad 
/Konrad 76/ and N.Klein /Klein 78/ from 
the Technical University in Berlin were 
I Presently: 
Universitat Stuttgart, Institute 
fur Informatlc, Herdeweg 51Raum 4, 
Postfach 560.7000 Stuttgart, FRG 
2 Presently: ° 
Simon Fraser University, Dept. of 
Computing Sci., Burnaby, B.C. Canada 
the starting ~oint in the e!abor~tion of 
this module. 
Presented version of the system was 
implemented on the IBM 370 computer /~.I 
370 operating system/. 
2. Transformation of natural language 
sentences into logical formulae 
The user of the DIALOG system intro- 
ducing his utterance into the system 
comes into direct contact with the natu- 
ral language analysis module. This mod- 
ule plays the key role in the machine 
natural lang1~age communication process. 
Similarly as in many other information 
systems of this type, e.g. L\[fMAR /Woods 
72/, PLANES /Yaltz 76/, SO~FIF, /~urton 
76/, RENDEZ-VOUS /Codd 78/, PLIDIS 
/Berry-Rogghe 78/, OIALOGIC /Grosz eta\]. 
82/, the purpose of the module is to 
transform a text in the natural language 
into a chosen formal representation. Suc~ 
Such a representationmust meet a number 
of requirements. Firstly, it must be 
"intelligible" to the internal parts of 
the system, i.e. the deductive comoonent 
and/or managing the data base. Secondly, 
it must carry in a formal, and clear man- 
er the sense and meaning of utterances 
in natural language. Finally, the repre- 
sentation should allow for a reproduc- 
tion of the original input sentence with 
the aim of generating intermediate para- 
phrases and/or answers for the user. 
In the parser of the DIALOC, system, 
we attempted on the gratest, in our 
opinion, achievements in the field of 
natural language processing. The follow- 
ing works had the greatest influence on 
the final form of the module: /Berry- 
-Rogghe 78/, /Bates 78/, /Carbonell 81/, 
/Cercone 80/, /Chomsky 65/, /Ferrari 
80/, /Fillmore 68/, /Gershman 79/, 
/Grosz 82/, /Lnndsbergen 81/, /Marcus 
80/, /Martin 81/, /Moore 81/, /Robinson 
82/, /Rosenschein 82/, /Schank 78/, 
/Steinacker 82/, /Waltz 78/, /Wi\]ensky 
80/, /Woods 72/ and /Woods 80/. We have 
transferred, with greater or less suc- 
cess, the most valuable achievements 
presented in these works, pertaining 
t96 
mainly to the English language process- 
ing, into our system, using them in the 
treatment of the Polish language. We 
attempted thus, to preserve a certain 
distance with regard to the language it- 
self, as well as the subject of conver- 
sation with the computer, so that the 
adapted solutions were of a broader 
character and through that became com- 
parable with the state of research in 
that field in other countries. 
2.1.The role, wlace and structure of 
the language analysis module 
The purpose of the language analy- 
sis module in the DIALOG system is tran- 
sformation of the user's utterance /in 
Polish/ into the I order logic formulae. 
Other formal notations such as II order 
logic formulae, FUZZY formulae, Minsky 
frames and even the introduction of 
intensional logic elements are also con- 
sidered. At present, ~e will concentrate 
on the process of transforming a natural 
sentence into a I order logic formula. 
The system is equipped with two 
independent modules: deduction and data 
base management. The data for these mod- 
ules are the formulae generated by the 
parser. We will present only one module 
working on the basis of the we~( second 
order logic. 
The parsing system consists of the 
two closely cooperating parts: a syntac- 
tic analyser and a sem(nntlc interpreter. 
The whole was programmed with the aid o~ 
a mechanism called CATN /Cascaded ATN/ 
/Woods 80/, /Bolc, Strzalkowskl 82a,82b/ 
/Kochut 83/, where the syntactic compo- 
nent plays the role of the "upper", i.e. 
the dominating "cascade". For the syn- 
tactic analyser produces a structure of 
the sentence grammatical analysis, which 
in turn undergoes a semantical verifica- 
tion. In case, where the semantic inter- 
preter is not able to give the meaning 
of the sentence, the syntactic component 
is activated again with the aim of pre- 
senting another grammatical analysis. If 
such an analysis cannot be found, the 
input sentence is treated as incorrect. 
2.2. The syntactic analyser 
The syntactic component of the par- 
ser produces a gra~natical analysis of 
the input sentence in Polish. This was 
possible due to a skillful programming 
of rules governing the morphology and 
syntax of the language. Although, the 
whole system was oriented towards a de- 
fined type of texts /medical/, the ac- 
cepted solutions make it a much more 
universal tool. We do not claim that the 
syntactic analyser in its present fol-m 
is able to solve all or the majority of 
problems of the Polish language syntax. 
It includes, however, rather wide subset 
of the colloquial language, enriched by 
constructions characteristic for medical 
texts. 
A natural language sentence intro- 
duced into the parser undergoes firstly 
a pretreatment in a so called spelling 
correcter. If all the words used in the 
sentence are listed in the system vocabu- 
lary then the sentence is passed for syn- 
tactic analysis. Otherwise the system 
attempts to state whether the speaker 
made a spelling error, giving him a 
chance to correct the error and even 
suggesting the proper word, or whether 11e 
used a word unknown to the system. In the 
last case, the user has a possibility of 
introducing the questioned word into the 
vocabulary but in practice it may turn 
out to be too troublesome for him. Usual- 
ly then, the user is given a chance of 
withdrawing the unfortunate utterance or 
formulating it in a different way. 
The proper syntactic analysis begins 
at the moment of activating the first 
"cascade" of the parser. It consists of 
five ATN nets, with the aid of which the 
grammar of the subset of the Polish lan- 
guage has been written. The two largest 
nets SENTENCE /sentences/ and N0\[~-P_RR 
/nominal groups/ play a superiorrole in 
relation to others: ADH-PT~A /adiective 
groups/~ ADV-PT~A /adverb groups2 and 
Q-EXPR /question phrases/. The process of 
syntactic analysis is usually quite com- 
plex and uses essentially the non-deter- 
ministic character of orocessing in ATN. 
It Is justified by the-specific nature 
of the Polish language, which is charac- 
gerised by a developed in~ection and a 
Sentence free word order. 
The result of the syntactic analysis 
is a grammatical analysis of the input 
sentence in the form of a so called 
o-form. It is a nonflexional form of 
a sentence, ordered according to a fixed 
key. The construction of the o-form can 
be expressed ba the structure: 
<o-form~ : := 
(S (questiqns) i (negation~ I (modalitie~ 
l(predlcate/verb)l (vague~ I (subject) ! 
~direct objectS_| (indirect object> I 
~(pre~. phrase)I}"(CAUSE/RES\[~(o-forn~\] END) 
The stick mark "|,, is usually used as a 
symbol of the meta-language. Here it is 
used as a symbol of the defined language. 
Symbols S and END comnrise a single 
clause. A clause expresses every elemen- 
tary activity or event expressed in the 
197 
input sentence. Often, the o-form has 
a richer structure than a classical 
analysis tree. The elements of the 
o-form called ~subject~ , (direct ob- 
Ject~ , (indirect objectS, and ~adJect- 
ive phrase) can also be expressed or 
modified with the use of clauses. The 
stick marks "I" separate the parts of 
the o-form and are its constatnt ele- 
ments. Then transformed nuestion is 
subjected to semantic interpretation. 
The syntactic analyser manages the 
vocabulary, where infle×ional forms of 
words are kept. The vocabulary defini- 
tion specifies the syntactic categories, 
to which given words belong. It also 
describes forms of words with the aid of 
lexlcalparameters: case, number, person 
and gender. These parameters are of gret 
value in examining the grammatical con- 
struction of sentences. 
2.3. The semantic interpreter 
When the syntactic analysis is suc- 
cessfully completed the o-form of the 
input dentence is forwarded for the sem- 
antic interpretation. The syntactic 
"cascade" is suspended, i.e. removed 
from the operational field, leaving 
place for the semantic "cascade". The 
configuration of the removed "cascade" 
is remembered thus, in case of necessity 
of generating an alternative grammatical 
analysis. 
The semantic interpreter consists 
of the two main parts: a constant con- 
trolling part, working on the basis of 
a very general pattern adjustment, and 
compatible experts algorithms, where 
the knowledge of the system in the field 
of conversation has been coded. The pro- 
cess of interpretation is assisted by 
a special vocabulary of semantic rules 
and on additional vocabulary complement- 
ing the expert knowledge. 
The sentence in the o-form is for- 
warded directly to the controlling part 
of the interpreter, where such its par- 
ameters as time, negation, aspect .... 
are evaluated first. Then the central 
predicative element of the sentence 
"calls for" a proper semantic rule, 
which from then will guide the interpre- 
tation process. The rule has a form of 
~ pattern-concept pair /Wilensky 80/ 
Gershman 79/, /Carbonell 81/, where ~he 
pattern reflects the scheme of an ele- 
mentary event, wheras the concept indi- 
cates how its meaning should be express- 
ed through formulae. The semantic rule 
is activated for the time of interpre- 
tation of a single clause. If the pat 
tern is adjusted to the cl~use, an 
atomic formula is generated, expressing 
the meaning of the clause. The meaning 
of the whole sentence is expressed as 
a logical combination of meanings of all 
the o-form clauses. The semantic rules 
bring different /on the surface/ descrip- 
tions of the same phenomenon into a com- 
mon interpretation. 
The.general structure of formulae 
generated by the interpreter is ex- 
pressed by an implication: 
41^~2^ ...^~n-~ 
"where ~ has been introduced from a sem- 
antic rule and~i come from the system 
knowledge - special compatible parts 
of the interpreter called the experts. 
Individual o-form phrases, in the con- 
text of the dialogue subject, are inter- 
preted in experts. 
In our system, designed for conver- 
sation with a phlsician, we have experts 
for names of sicknesses /SICKNESS/, 
names of ~rgaus /ORGAN/, internal sub- 
stances /oUBSTANCE/, therapies /TREAT- 
~NT/, medicaments /MEDICAmeNT/ and 
names of animate objects /ANIMATE/ and 
the remaining objects foreign to the 
body /PHYSOBJ/. Experts are activated 
on the request of a proper semantic rule. 
The controlling part of the inter~eter 
"instructs" the expert/s/ chosen by the 
pattern to interpret a notion or expres- 
sion. The indicated expert can solve the 
problem on its o~m or seek for the help 
of other experts. Often, one complex ex- 
pression has to be gualified by two or 
three exprrts. 
All the experts, as well as the 
controlling part of the interpreter 
/FOR~UJLA, CASES and QWORDS nets/ have 
been recoreded in ATN formalism and form 
a lower "cascade" of the parser. 
The interpreter is also egulpped 
with a mechanism of context pronominal 
reference solution. 
2.4. Examples of transformation of a 
medicaltext into logical formulae 
We will present two examples of 
transformation of medical sentences into 
I order logic formulae. Before that, 
a few words on the adopted convention of 
formula notation. The symbols IMPLSYM 
and KONJSYM are logical operators 
/implication/ andS/conjunction/ re- 
spectively. Integer placed directly 
after the symbol KONJSYN indicates the 
number of conjlmction factors. Names of 
predicates are preceded by symbols '~" 
7hash mark/, and an integer placed right 
to the name defines the number of predi- 
198 
cate arguments. The arguments specify 
their type /sort/, name of the variable 
and constant /if there is one/. 
Example 1 
Sentence : 
Alkehol powoduje r6wnie~ wzrost napi~- 
cia mi~ni6wki dwunastnlcy. 
/Alcohol also causes the rise of the 
tenlcity of the duodenum muscular 
coat/ 
o-form: 
(s DC~ I I I I ~O:'IODOWAC I RO~VWIEZ I 
A~KOHOL I s ! II I WZR0ST III NAPIECIE 
MODIFIERS NIESNIOWFA DWU~fASTNICA 
~I ~ END J I I END) 
formula: 
(If.TPLSYM 
(KONJSYM 3 ((~tBADf.TE, DIC 1) (r.~OlO X44)) 
((I~I~EDICIC.,'E, NT 2) (P.~DIO X44) (f.S~A~TE 
X45 ALJ<OT-TOL )) 
(IMLSYM 
(KONJ~ +(~','~YDZ-NARZA~ I) 
(ORGAN X+9)) 
((~0RGAN 2) (ORGAN X+9) (0NINE 
X50 DWUNASTNIOAB 
((~PART-OF-ORGAN 3) (BODY X48) 
(PNAME X51 NIESNIOWKA) 
\[ORGAN X49 )) 
((# SICKNESS 4)(SIeIC X47) 
(STYPE X52 FIZJ) 
(SNAME X53 NAPIECIE) 
(BODY X48))) 
((SRISE 2) (SYI,TPTON X46} (SYI.FPTON X47) 
((~IMPLY 3)(INFER X43)(P-~EDIC X4:4J )}J 
(SIOKNESS X46))) 
Example 2 
Sentence : 
Czy alkohol mo~e by6 przyczyn~ 0ZT? 
/Can alcohol be the cause of acute 
pancreatitis ?/ 
o- form: 
(S CZY II N0C I I BYO II AI:KOHOL I 
PRZYCZYNA ~'ODIFIERS 0STR. ZAPALENIE 
TRZUSTKA I II END) 
formula: 
(NIL (T~T,SYM 
(KONJSIq,~ 6\[(aVAGI~ 2) ~CTION X69) 
(VAG XTO M00)) 
((UBADfTDIO I) (MEDIC X71)) 
((~MEDICAHENT 2) ~.\[EDIC X71) 
(~A~\[E X72 ALKOHOL)) 
((~ORGAN 2) (ORGAN X74) 
(ONm+~, X75 TRZUSTKA)) 
((~'~DZ-NARZAD I; (ORGAN XV4) 
\[~SIC\]~fESS 4)(SICK X73) (STYPE 
X76 PATO) (SNA~ X77 OZT) 
{BODY X76 ))) 
\[(:~IMPLY 3) (INFER X69) (ETIO X71) 
(STOKNESS x73)))) 
. The deduction and knowledge repre- 
sentation module 
The deduction module is a separate 
part of the whole DIALOG system. Its maiz 
purpose is to collect and represent the 
knowledge gained by the system and also 
the ability to use the possessed infor- 
mation in accordance with the wishes of 
the user of the system. 
Our work on the achievement of the 
objectives indicated above was based on 
the experiences pre~ented by E.Konrad 
and N.Klein /Konrad 76/, /Klein 78/ from 
Technical University in West Berlin. 
In the previous chapter we present- 
ed how the text, written in Polish, is 
transformed into I order logic formulae. 
This, of course, implies the way of rep- 
resentation of the knowledge presented 
in the natural language. 
3.1. Knowledge representation 
The information included in the 
logical formulae coming from the lan- 
guage module has to be stored for later 
use. The logical formulae are then in- 
troduced into the data base. The data 
base, adequately filled with the men- 
tioned formulae, constitutes the knowl- 
edge represenlation carried through the 
natural language sentences. It is as 
equivalent to the text as the I order 
logic allows to convey the meaning of th~ 
natural language sentences. 
Data Base 
The date base consists of three sep- 
arate parts: a nucleus, ~ amplifier and 
a filter /Konrad 76/. Each of the parts 
includes a different , from the concep- 
199 
% 
tional point of view, elements: 
A. The nucleus includes groud literals, 
which represent facts occuring in the 
field of knowledge represented in the 
base. E.g.the information that the pan- 
creas is a secretory organ is presented 
as a literal 
(~ WYDZ-NARZAD (TRZUSTtfA)~ 
From the system point of view there is 
no conceptional difference between the 
tee facts: the above one,and 
(ORGAN (\[nRZUSTKA)) 
Thus the type /sort/ ORGAN may be re- 
garded as a predicate and the above 
atomic formula as true one. 
B. The amplifier is a part representing 
the "fundamental" knowledge of the 
system. The formulae included in the 
amplifier can be devided into three cat- 
egories: 
I/ dependent formulae 
/i/Vx~ ~s~..VXnCS~ A~x~,.. ,x~,Ixf=~ 
A is here any formula and n a predi- 
cate. As we can see each variable, 
bound by the universal ~uantifier is 
of a specified sort. 
2/ independent formulae 
/ii/ ~XlrSS...~Xn(S \] ~(Xl,...Xn) 
3/ restrictive formulae 
/iii/Vx 1Cs\]... ~ XngS\] l~(xl,...,x n) 
The majority of the formulae generated 
by the language analysis module is of 
the /i/ form. 
C. The filter contains the formulae 
representing the Imowledge necessary 
to preserve the integrity of the data 
base. 
FILTER 
NUCLEUS 
AMPLIFIER 
RESULTS I 
.~. MODIFYING I 
CO~ANDS I I 
l i 
INTERPRETER 
Fig. I. Diagram of the data base 
system /Konrad 76/ 
Recapitulating, the nucleus repre- 
sents the extensional part of the know- 
ledge represented in the data base. It 
is the fundamental knowledge which can- 
not be obtained from the amalysis of the 
presented text, and which is assential 
to proper deduction. The amplifier 
represents the intensional part of the 
data base. The knowledge represented 
there is a co31ection of statements used 
for deduction. 
Each of the logical formulae is 
kept in a certain internal form, corre- 
sponding to the way of deduction, de- 
scribed later on. As we have already 
mentioned, the majority of formulae is 
of the /i/ form. Every such formula is 
converted, at the moment of inserting 
into the data base, to a pair of the 
following form: 
(~conclusion~premises testing procedure) 
3.2. The knowled6e extraction 
Because of the menner of storing 
the knowledge described in the point 3.1, 
the answer to the question presented to 
the system does not have to be represent- 
ed explicite in the data base. The de- 
duction module should be able to obtain 
all the information included in the data 
base. 
The questions presented to the sys- 
tem are also converted to the logical 
formulae. Thus, the extraction of knowl- 
edge is reduced to the verification of 
a given formula towards the present con- 
tent of the data base. 
The logical formula representing 
the question is converted to an appro- 
priate LISP form. Evaluation of such 
a form is equivalent to examination 
whether the represented by it formula is 
true. This form correspond to the normal 
form of the logical formula /LISP func- 
tion AND, OR and NOT are used/. The 
literals are tested by a TESTE function 
according to the following algorithm: 
I. Check the amplifier, trying to find 
the rule with the conclusion unifi- 
able with the literal under proof. If 
such a formula does not exist that there 
is no proof of a given literal; 
2. If there is such a formula then: 
a. if it is indicated as an indepen- 
dent formula then STOP with a proof 
b. if it is indicated as a restrictive 
formula then STOP without a proof~ 
c. otherwise evaluate the form asso- 
ciated with the conclusion; if we 
obtain NIT, /false in LISP/ then 
search the amplifier for another 
rule and go to 2. If we obtain 
value different than NIL then STOP 
200 
I 
with a proof. 
Otherwise Stop without a nroof. 
It is therefore a so called backward 
deduction zystem. The nroof goes back 
from the formula - aim ~ to the facts, 
applying the formulae from the amplifier 
in the "Backward" direction. 
The answer can be YES or NO or it 
can be a list of constants depending on 
the kind of question. 
The I order logic has been enriched 
here with some elements of the II order 
language. Predicate variables, quantifi- 
cation of these variavles and retrieval 
of predicates as well as constants have 
been introduced. 
3.3. Access to the data base 
The system communicates with the 
data base through commands of the spe- 
cially designed language. These commands 
enable introduction and erasing from the 
data base. 
The basic commands serving the pur- 
pose of knowledge extraction are TEST 
and FIND: 
a. TEST A 
- looking for the proof of a formula 
A. Answer YES/NO. 
b. FIND ~1""11'mX~xl"'xn) ~r~1"';x1" '~ 
~i - predicate variables 
- retrieval of all the pairs: m-tuple 
predicates and n-tuple oe constants 
which satisfy a given formula A. 
3.4. Example 
The formula presented in the 
example I and a formula below have been 
introduced into the amlifier. 
Sentence: 
Wzrost napi@cia mi~dni6wki d~mnastnicy 
mo~e by4 przyczyn~ OZT. 
/The rise of the tonicity of the 
duodenum muscular coat may be the 
reason of acute pancreatitis/ 
Formula: 
(IMPLSYM 
(VAC x84 ~oc)) 
~,~LS~ 
DW~ASTNIOA~ 
,((~ART-OF-OROAN ~)(~Y xe~) 
XgO r IESHIO   A) 
(ORC N X88)) 
L@szcm~;ss 4) (szc~ ×s6)(S~E X91 
FIZJ) (S~A~, X92 
~APIECIE) \[~0~\[ ~S7))) 
(@ OROAN 2\] (ORGAN X94) (O~Tm~ X95 
TRZUS~KA); 
\[(# ~:~/DZ-NARZAD I) (ORGAN X94) 
((~ SICKNESS 4) (SICK X95) (STYPE X96 
PATO) (SNA~:E X97 OZT)(BODY X94))) 
((II~$PLY 3)(INFER X85\] ~TIO X85) 
(SIC~<~TESS X95))) 
Formula corresponding to the question is 
presented in the Example 2. The ampli- 
fier contains the formula describing 
transitivity of the predicate I~LY. 
Facts - ground literals - were 
introduced into the nucleus. E.g. 
((~BAD~DIO (ALI(O~OL)) , 
(WV~DZ-NARZAD (DWUNASTNICA)), etc. 
After converting the formulae of theorem~ 
and question into the LISP form its 
evaluation Will find the answer to the 
question. The answer is of course YES. 
4. Conclusion 
The results obtained during the 
work on the system confirmed our direc- 
tion of research. Our further work will 
concentrate on constant improvement of 
the existing modules. At the sere time 
we will undertake attempts of enriching 
the system with better deductive modules 
such as resolution in modal logic, 
default reasoning /Relter/, FUZZY and 
Minsky frames. 
ACKNO~WLEDGEMENTS 
The medical text was prepared by 
a team of physicians from the Post- 
graduate Education Center in Warsaw 
under the leadership of Prof. Dr 
J.Doroszewski. Prof. Doroszewskl and his 
associates have been giving us constant 
assistance in the interpretation of the 
medical knowledge included in the pre- 
sented text. Due to their creative and 
active cooperation we were able to 
undertake the elaboration of the de- 
scribed system. We would like to express 
our cordial gratitude to Prof. Doroszew- 
ski and the whole team of doctors. 
201 
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