Conference Internationale sur le traitement 
Automatique des Langues. 
MANTAIDED COMPUTER TRANSLATION PROM ENGLISH 
INT0 FRENCH USING AN ON-LINE SYSTEM TO F~NIPULATE 
A BI-LINGUAL CONCEPTUAL DICTIONARY, OR THESAURUS. 
Author: MARGARET MASTERMAN 
Cambridge Language Research Unit, 
20, Millington Road, 
CAMBRIDGE. 
ENGLAND. 
Conference Internationals sur le traitement 
Author: Margaret Masterman 
Institute: Cambridge Language Research Unit, 
20, Milllngton Road, CAMBRIDGE, ENGLAND. 
Title: MAN-AIDED COMPUTER TRANSLATION FROM ENGLISH INT0 
FRENCH USING AN ON-LINE SYSTEM TO MANIPULATE 
Work supported by: Canadian National Research Council. 
Office of Naval Research, 
Washington, D.C. 
background basic research 
@arlier supported by: National Science Foundation, 
Washington, D.C. 
Air Force Office of Scientific 
Research, Washington, D.C. 
Office of Scientific andTechnical 
Information, State House, High 
Holborn, London. 
~.B, References will be denoted thus: ~D~ 
I. Long-term querying of the current state of 
despondenc ~ with regard to the prospects of 
Mechanics1 Translation. 
The immediate effect of the recently issued 
Report on Computers inTranslation and 
L~istics. LANGUAGE ~ MACHINES ~J3 has 
been to spread the view that there is no 
future at all for research in Mechanical 
Translation as such; a view which contrasts 
sharply with the earlier, euphoric view that 
(now that disc-files provide computers with 
indefinitely large memory-systems which can be 
quickly searched by random-access procedures) 
the Mechanical Translation research problem 
was all but "solved". 
It is possible, however, that this second, ultra- 
despondent view is as exaggerated as the first 
one was; all the more so as the~ is written 
from a very narrow research background without 
iny indication of this narrowness being ~iven. 
F~r example, an M.T. Thesaurus has never yet 
been put on a machine; (_~ and the analogy between 
M.T. and Information-Retrieval has never yet been 
explored, (yet retrieving a translation in res- 
ponse to a user's request is basically the same 
as retrieving any other piece of information in 
response to a user's request~ ~ No mention, 
moreover, is made in the Re~ort of the work of 
.2. 
(e.g.) Dolby and Resnikoff in analysing the nature ~ 
structure of natural-language dictionaries, 
nor is any recommendation made that more of 
this evidently necessary work should be done.MoTeO~£/~ 
~he need for basic research into the trueproblem 
posed by the ambiguity and extensibility of in- 
dividual language-signals of any order of length, 
and the connection of this with other learning- 
problems and character-recognition-problems~ has 
never yet been faced. In fact, the situation is 
worse; a particular application has been pronounced 
useless and/or impossible before the general field 
of examining the basic semantic nature of human 
communicationhas been created. 
II. R0commendation: do not look at the theoretic com- 
plexities of current researches into language- 
problems: look rather at the techuolo~ical advances 
which have alread 2 been made. 
Thus the basic recommendation given in the Report, 
nalely that practical research into Mechanical 
Translation should be discontinued, while present, 
very narrowand fragmentary trends of "pure" 
theoretic linguistics research should be supported, 
can be queried both ways round. For the advances 
in this field are precisely comlmg from the tech- 
nologies, as the Report itself shows, and that in 
several areas i) Thus computer-tTpe-setting, in 
which hyphenation can be done with a "logic", that 
is, without a dictionary, is now an accomplished 
fact ~ ii) within information retrieval, mech- 
anized retrieval systems of increasing sophisti- 
cation and efficiency, are being constructed for 
practical use at Universities and within industry: 
iii) synthetic speech considered as synthetic 
message, - passed over in the Report because 
created by telephone engineers and not by linguists, 
- is making great strides ahead; iv) high-level 
programming languages increasingly operate more 
llke natural languages, so that the machine can 
pick up and process something more like the user's 
normal way of thinking; v) the Mannheim and 
Luxembourg machine-aided translation-systems are 
acknowledged in the ~ to save 40 - 60 per 
emat of a translator s time; 6(~3 and vi) research 
in automatic character-recogniti0n has now reached 
such a point that consideration of the extent to 
which this will slash M.T. costs and increase M.T. 
usefulness should have not been ignored. C~ 
III. Report on an actual experiment in man-alded M.T. 
The experimental work to be reported on in this 
paper and which is still in progress, is the 
.3. 
development of a computer-aided procedure for the full 
translation of one single paragraph of governmental 
report-style English into governmental-report-style 
Canadian French, to be made in such a way that the 
translation actually produced accounts for the 
non-literal translation which was actually made by the 
official Canadian Government Translator. 
The philosophy behind this research is that before 
employing automatic-translation-devices on a large scale, 
~ou.have got to understand what translation is yourself! 
Just as before building a liner-smoke-funnel you have 
got to understand wind-flow. You may not in the end 
use, to assist translation, all the mechanical procedures 
which you develop in order to understand translation, 
but you have got to know what these are6mechanic~lly 
speaking, you have not got to be continually surprised 
and taken aback by what the human translator actually 
does. 
~ven the amount of experimentation which we have 
performed so far has i~ufficed to convince us that nobody 
does knww, in terms of automatic procedures, what 
translation is. So-called ~¥~pEegrams~up to now, 
though they have performed e~ more or less sophisticated 
feats in bi-lin~Aal transformation of individual words 
and of individual constructions, have never in the true 
sense of the word, translated anything. 
We ~ave m~w, ~wever, started to put on a machine a 
more realistic translation-model of the following form. 
The model draws on ii) iii) iv) and v) of the tech- 
nological devices mentioned above, i) As is standarg 
practise now on Information Retrieval, the model uses 
a Thesaurus. This Thesaurus, however, is not merely 
an Information-Retrieval-type Thesaurus of terms, but 
a"Roget's Thesaurus" type of technical dictionary, 
though of a novel kind. ii) The retrieval-procedure 
works by using as its "requests" a unit longer than the 
word, and which has been called a "phrasing" (Frz 
rh~hmiaue); ~ a computer-program, (written 
J. Dobson for the Titan Computer at Cambridge University 
Mathematical Laboratory) now exists which derives phrasings from Written text (see appendix A) iii). The 
user is on-line to a computer, on which the whole 
Thesaurus is Stored; andhe reacts with this Thesaurus 
by means of question-and~answer routines operating in 
real time which are programmed into the machine by us~ 
the very sophisticated programming language T.R.A.C. ~9~- 
Anl v), the experiment presupposes the validity of the 
result that, in operation, the computer-stored diction- 
aries at Luxembou2~an~ Trier (to which the user is not 
on-line and with which he cannot therefore react, ) 
.4. 
already, in spite of these limitations save 40-60% 
of the translators' time. It is inferred from this 
that on-line use of more sophisticated dictionaries 
by man-machine interaction in the conversational mode 
is the right way, from now on, for M.T. research to go. 
III. The Basic Principle of the Man-Machine interact!on. 
The input to the machine is a stressed and contoured 
phrasing, i.e. a phrasing with some stresses marked and 
minimal syntactic naming of the constituent words. 
Research to produce this input mechanically, by a 
phrasing-stresser-and-parse~ is currently being supported 
by the Office of Scientific and Technical Information, 
London; at present the program (Mark II) segments the 
text into phrasings mechanically, but does not either 
mark the stressed words or provide any snytactic naming. 
(see Appendix A). In the mini-demonstation of the ~an- 
maahine interaction, therefore, (the only one which is 
already operational as a machine,) the operator at present 
types in a single phrasing at a time minus the stressed 
words, which have been pre-marked on his text. Thus, he 
does not type in a complete phrasing, but what we have 
called a phrasing-frame. (Later the machine will compute 
the phrasing-frame from the text~ Examples of assorted 
phrasing-frames are given below: 
ASSORTED PHRASING-FRAMES 
~'" I~'~\] ~o~ .......... \[~ou~j 
T~ is A ..... t~6~\] i~ T~ .......... E~ou~1 
HE WENT A TO THE .......... \[Nou~\] 
..... ~&~a6~ ~6~ ¢) 
o~ ....................... () \[ABST~O~ ~ou~\] 
............ 
ANY..4~ ..... 
Aeoeoeooeeeteeeo. 
() 
.5. 
SUCH AS ........... IN ...... 
MUST BE PARTICULARLY~.. 
..... C sl 
TO ................. ITS ........... 
~VERB INFIN~ \[NOUI\] 
key: ........... 
() 
stressed word omitted 
silent beat 
A do not translate though stressed. 
N.B. Other markers e.g. ~he marker J to set in 
operation a routine to inter-connect syntactiaally 
connected phrasings will be discussed in a further 
publication 
On receiving the phrasing-frame, the machine questions 
the opea~or in order to make him specify further, from 
his general knowledge of the text and of its subject, 
what the cOntext of the particular phraslng-frame is. 
The example given below, in which is progressively 
specified the correct French translation of an English 
~erb of motion (one of the notoriously difficult ~lish 
forms to translate into French) shows how complicated 
this questioning can be. Not more than three rounds of 
questioning are allowed, and when the operator has 
produced his specification, the unique correct trans" 
latio~f the frame is stored in the immediate-access 
store~.~chine (see Appendix B). In the example set out 
below, however, the differ~ French translations of all 
possible answers obtainable under Round II and Round III 
of the inter-action are set out immediately underneath 
the English statements which the machine would actually 
print out on the console, in order to show the under- 
lying reason for the whole enterprise. 
STAGE I: 
ROUND X 
Operator types in the phrasing-frame: 
HE XX-ed TO THE XX 
Machine asks: DO YOU MEAN 
A HE MOVED TO SOME POINT ? 
.6. 
B HE DETERIORATED (idiom)? 
C HE CROSSED OVER SOME OBSTACI~ ? 
D HE COMMUNICATED WITH SOME BEING ? 
Operator answers: A 
ROUND II 
Machine then asks: DO YOU MEAN 
A HE 
Ii 
B HE 
Ii 
C HE 
II 
D HE 
Ii 
E HE 
Ii 
DEPARTED TO SOME DESTINATION ? 
partit pour ... 
TRAVELLED TO SOME DISTANT REGION ? 
s'en alia h ... 
FLED TO SOMEREFUGE ? 
se r@fugia chez ... 
ARRIVED AT SOME DESTINATION 
azriva h ... 
VISITED SOME PLACE ? 
visita ... 
Qperator answers: 
,ROUND III 
,Machine then asks: HOW DID HE TRAVEL? 
A BY 
I1 
B BY 
II 
C BY 
Ii 
D BY 
" II 
E ON 
II 
F BY 
Ii 
G BY 
I1 
Operator 
PLANE ? 
prit i ' avion pour ... 
SEA ? 
voyagea par bateau ~ ... 
ROAD ? 
voyagea en voiture ~ ... 
TRAIN ? 
prit le train pour ... 
FOOT ? 
se rendit ~ pied ~ ... 
BICYCLE ? 
s'en allah bicyclette ~ ... 
SWIMMING ? 
alla ~ la nage ~ ... 
answers: A .7. 
STAGE TWO 
The o~erator then types in the two stressed words: 
FLEW and FRONTIER 
The machine then dictionary-matchesa~.d resolves: 
FLEW = XX-ed = ALREADY TRANSLATED: DELETE 
XX = FRONTIER = FRONTI~RE (f) 
and immediately, for the text: 
He flew to the frontier 
The Machine prints out the translation; 
IL PRIT L'AVION POUR LA FRONTI~RE 
0 
Detailed examination of this example shows that ~ 
hind this particular way of making an on-line system 
teract with an operator there lies a strategy, a 
hyDothes~s and a ~ros~ect, 
V. The strategy is at all costs to avoid post-editing; 
but to allow maximal pre-processing of the input text 
by the machine interacting with the operat.or, all the 
question-and-answer routines being in the operator's 
native language. 
Th@ argument against post-editing (as the U.S. Report 
conclusively shows) is that it is either mechanical 
e.g. the resolution of French gender-concord - in which 
case the machine itself can be programmed to do it - 
or it is creative and/or intuitive;in which cgse it can- 
not be done at all without extensive reference back to 
the input text~ho could interpret "Shakespeare Overspat", 
which was the title of a Russian "Pravda" article as 
translated by the U.S. Air Force ccmputer~ The real 
meaning was "Shakespeare is now a back number"), in 
which case the post-editor might as well have translated 
the whole text h~self in the first place. 
To avoid post-editing, however, the output produced 
by a man-machine reactive M.T. program has either got 
to be a blamk space (when the program fails), or a 
unique translation which is known to be correct. Now 
uniqueness of output can be brutally produced, as every- 
body knows~programming the machine only to print out 
one eg any set of alternatives. Correctness, however, 
can only be achieved by the target-language translation 
having been approved beforehand by the operator, from ~: 
cues which the machine gives him, or which he gives the 
machine - i~ his own language; i.e. in the source 
language. The real use, therefore, of the three-stage 
question-and-answer routine exemplified above, is that 
it enables an Englishman with a console but who does 
.8. 
not know any French to produce a unique and correct 
idiomatic French translation of an English textrprovided 
that he is prepared to take the trouble to pre-process 
the English text so that it is finally restated in a 
Frenchified sort of way. After this the machine can 
of course transcribe it into French. 
In other words, a machine-aided translation program 
basically consists - 
a) of programming the machine to pick up t~e ambiguities 
in the source language which the target-language 
will not tolerste (not the other way round) and 
of making the operator produce the additional 
information which will resolve them. 
Take, as example, the phrasing 
/for a standb2for~. 
This looks technical and unambiguous in the English, 
but comparative examination of bi-lingual text showed 
that it translated into French (and in the same document) 
as either 
i)/d'une force d'urgence~ i.e./"of an emer~ency force/ 
or il) /pour une force de r6serve/ i.e. /"for a reserve 
force"/, 
according to sophisticated considerations of context. 
Therefore, when the operator types the technical term 
STANDBY FORCE into the machine, in order to fill up the 
gaps in the phrasing-frame /FOR A .......... \[NS~\] \[AdjJ 
the machine has got to answer him back: 
DO YOU MEAN 
A AN EMERGENCY FORCE 
B A RESERVE FORCE 
The operator then has to choose, and type back into 
the machine the alternative he wants, after which the 
machine can make the translation. 
b) 8imil&z~,,.~ way mustmbefound~ef emab~ng the 
machine to pick up, from cues in the source language, the 
metaphors and idioms which the target-language will not 
tolerate/and to assist the operator to rephrase the 
stretch of text concerne~d~in terms which the target- 
language will tolerate~he difference between idioms 
and metaphors is that idiems can be mechanically picked 
up and matched by an idiom dictionary, whereas metaphors 
can't. 
c) Similarly again, the machine must be programmed to 
pick up, from the source language input, the con- 
structions which the target-language will not tolerate, 
and assist the operator to transform these into con- 
structions which the target-language will tolerate 
(e.g. to turm English passives into FreL~ch actives, 
and the adjectives of English adjective-noun strings 
into French post-positioned prepositional phrases). 
Thus the whole translating work, really, is done 
within the source language. Once you can preprocess 
your English input into a Frenchified shape in the 
respects a), b), c), above, the machine can transform 
this Frenchified English, with no trouble at all, into 
elegant French. 
The strategic hope, of course, is that by analysing 
the printouts produced by a large number of sequences 
of such machine-man interactions, in translating many 
types of texts, we shall ultimately learn how to make the 
machine answer, as well as ask, some of the rounds of 
questions, (as is already being done in a whole range 
of machine "edit" programs), so that the machine shall 
progressively become able to do more of the Frenchifi- 
cation process for itself; thus finally producing, (if 
the machine ever became able completely to take over) 
exceedingly slow but reliable machine translation, - 
which could~subsequsntly again)be speeded up. 
Before further discussion of the extent to which this 
strategic hope is a real hope and haw much a mere pious 
aspiration, i.e. the prospect, I will now set out the 
kvpothesis (as opposed to the strategy) of the experi- 
ment. 
VI. The hypothesis which the translation-model gives is 
the following: 
ATranslation consists of the pairing of a phrasing, 
P7 ' in Language A, with another ~hrasing, P2 ~ in 
Language B, in such a way that PI ~ ~1~forms 
an analogy with PI A, in a sense of "analogy" which 
cam be ostensively defined intterms of the model. 
Thus translating a phrasing into another language is 
no different, (according to this translation-model) 
from defining it, producing a parallel-phrasing to it, 
reiterating or otherwise further specifying it, in the 
same language. ~ 
The advantage of the model is that unambiguous 
criteria of the formation of such a pairing can be given. 
Por any response given by the operator to a machine-ques~ 
tion will form such a ,pair: the first member of the pair 
will be the original phrasing, (in English), the second 
the chosen machine-specification (called by us a template) 
.10. 
also in English. Then another pair will be formed 
whenever the machine translates the operator's final 
choice of template into French; the first member of the 
pair in this case, will be the final template chosen, 
and the seoond member will be the translation into French, 
with the stressed words translated and inserted into 
their correct places. Then again, an intermediate pair 
may be formed of which each member is a template; the 
first member of such a pair will be a more abstract 
template chosen atthe first round of man-machine inter- 
action, while the second member of it will be the more 
concrete template chosen by the operator at the Second 
round of man-machine interaction; and so on recursively. 
Any such pairing formed by the translation model, 
whether between English phrasing and template, or between 
template and template, or between template and French 
phrasing, we shall call a semantic square. A philosophic 
discussion of the notion of semantic square is given in 
another publication ~. 
A semantic sauare (in terms of thls model) consists 
of the pairing of any two linguistic sequences P1 an.d P2, 
PI and P2 each having the following characteristics. 
i) each has two stressed segments (which when PI is 
paired to P2, form points of the square). 
ii) each has these embedded in some phrasing-frame, 
(which, when PI is paired to P2 forms the fram._.._! of the 
square). 
iii) each has been selected as synonymous @ith the 
other at least once,either by the operator or by the 
machine. 
Thus, according to the model, translation consists 
of sequential semantic-square forming, the sequence of 
semantic squares thus formed continuing until it is 
brought to an end by the machine printim~ out a square 
which has a target-language phrasing as its second ~amber. 
To make all this clearer, let us further develop 
the example of man-machine interaction given above>by 
assumin~ that the phrasing to be translated is 
/HE WENTto the ol~q~/, 
To translate this, the operator types in 
/HE...E AST~aDVER3~tO the.....8~/~ ~ 
and chooses, at the first round of questioning, the 
abstract template 
H~' COMMUNICATED WITH SOME ANIF~TE BEING 
.11. 
The first semantic aquare of this sequence formed by 
the model is thus: 
/HE wm+_._~o TH~Po~_~ 
/HE COMMUNICATED WITH SOME ANIMATE BEING/. 
The machine then asks: DO YOU MEAN 
A HE REVEALED-ALL TO THE ENEMY 
B HE TOLD-A~STORY TO SOME LISTENER 
C HE CONSULTED WITH SOME AUTHORITY 
The operator chooses A, thus forming the second 
semantic square in the sequence: 
/HE COMMUNICATED WITH SOME ANIMATE-BEING/ 
/HE HEVEAZm>-AI~ TO raRE E~Emr/ 
The operator then types in the stressed word /POLICE/ 
(to specify the nature of the enemy), and the machine 
then forms the final semamtic-square: 
/HE ~VE~mD-ALL TO THE 
d /IL TOUT RE~ELA AUX FLICS/ 
"FLICS" having been pro-chosen by the operator's choices 
of template from a bi-lingual tree-dictionary-entry for 
the English word "police" with nodes as follows: 
Ng:Xl lie coa~IAssariat' I 
Thus the sequence of semantic',~squares formed by 
this operation of. the model is 
HE WENT TO THE POLICE 
HE C-~---MMUNICATED WITH SOME ANIMATE-BEING 
2 HE COMMUNICATED WITH SOME ANIMATE BEING HE REVEALED-ALL TO THE ENEMY 
.12. 
HE REVEAlED-ALL TO THE ENEMY 
3 IL TOUT REVELA AUX FLICS----- 
This square-sequence, with its AB BC CD overlap of 
content, I will call the semantic deep-structure of 
the mode~s translation-operation, and the tree-structure 
given above I will call the semantic deep-structure of 
the dictionary-entry. 
The totality of semantic deep-structures given by 
the model is the modei ls ~otal semantic-field. 
V_~ This, stated in the briefest possible terms, is 
the hypothesis given by the model. Now as to the pros- 
pect of developing this line of research. 
The first thing to say is that the model makes clear 
the unsuitability of the ordinary digital computer as 
compared to a human being for performing translation. 
For in this translation-model the computer handles each 
phrasing of the input text as a separate unit, and forces 
the operator, by successive rounds of questioning, so to 
specify it that it can be translated unambi~aously into 
French. But the human being, who does not treat each 
phrasing of a text as a separate unit, but who uses his 
understanding of the sarlier phrasings of a text to 
~aide him in hls understanding of the later ones, does 
not have to ask himself nearly so many questions. A 
progressive learning-model of translation, then, is what 
is really required, rather than the present single- 
phrasing-matching model. On the other hand, the com- 
plezity which has to be introduced into the model to 
account for all the differing French translations which 
have to be made of a single piece of English, according 
to its context, this would have to be introduced into 
any effective M.T. program: since you cannot retrieve 
from any computerised data-system any data which you 
have not first put in. But this second t~pe of com- 
plexity can be put into the machine gradually, by 
feeding in data obtained from examining the inter- 
lingual correspondenc~in a large corpus of bi-lingual 
text. 
There is, however, another, muc~ deeper obstacle 
to developing this research, and that is that (as M.T. 
research-workers have for some time past muspected) 
bi-lingual dictionaries provide almost no clue to 
semantic deep-structure. 
Within the context of the present experiment this 
became apparent in examining the English word "deliber- 
ations". The examination began with the construction 
of a dictionary-entry-card of the following form: 
English: DELIBERATIONS 
French: ~ OELIB~Pd~TIONS 
This entry being queried (and the maker of it having 
defended himself by saying that "deliberations" was 
the only word he knew of in English which could really 
be translated by the corresponding word in French), it 
was checked with Vinay's Dictionary~1~which ~ave the 
entry /d~bats mp1, discussion/. However, w~en an 
investigation whs made of how it was act~lly ~ranslated 
in the corpus of text, it only occurred once, where it 
was translated "membres", as follows: 
English 
The illustrative and comparative 
materials presented 
may~helpful 
to the deliberations of this committee 
French 
Les donn~'es explicatives 
et comuaratives () 
se r~v~leront, peut-etre 
tr~s utiles 
pou--'~ les me-------mbres du comit~ 
Moreover, the tramslator, in translating it t~us, was 
quite right; not only because "utiles" in French, likes 
a concrete complement, but also because this is what 
the passage means. 
However, this t~a semantic deep-structure 
for the hi-lingual dictlonary-entry of ~deliberations" 
of the following form: 
.,. /.. \ ~-.. . 
AGENTS (WHO..~0OS~)I l~m A~T~AL ACT~ARTEFACT (ANIMATE INGS) II(oF 0H00a  ) 
(wHo CHOOSm) I I"les d±soa'ssions" I AC VI  ) 
"les membres/' \[ ~l "Deliberations" 
It becomes evident, then, that if we are to make a 
~r Chlne account for the translations~ which good human anslators actually produce~using the kind of modern 
which has been reported o~ this paper, the problem 
is that of finding the ~ structures of the dlc- 
tionary-entries from the data actually given by a bi- 
lingual corpus; for the construction of the square- 
forming templates must depend on these- that is if the 
template-glossary and the bi-llngual dictionary are to 
interlock. 
Present resmarch efforts are ~herefore being con- 
centrated on the problem of "f~rming up" the whole notion 
of semantic dictionary-entry deep-structure. 
.14. 
CONCLUSION 
In view of the great interest which has already 
been aroused by this experiment, its small scale and 
pilot nature must be emphasized. (Actual output from 
a trial run of the program is given in Appendix ~). 
It has been implemented only on an I.C.T. 1202 computer, 
with T.R.A.C. facility, to which a single keyboard 
has been attlched, just under the print-out, on which 
the machine's "replies" to the operator, as well as his 
"questions" appear. This machine has only 4K store 
with no back-up, and 2K of this is occupied by the 
T.R.A.C. facility; the rest of the store will therefore 
only hold enough Thesaurus to process an average of lO 
"phrasing-frames" at ~ny one time, so the sections of 
Thesaurus which are needed for any particular test have 
to be prechosen by hand fromthe larger deck of punched 
cards of which the Thesaurus, in its machine-readable 
form, consists. Even these cards, however, are only 
punched as required; the basic triple dictionary, from 
which the Thesaurus is being built up, is being stored on 
ordinary business equipment, (Twinlock Handi~e~inder 
HRA3 handled with a Shunic Signalling System ~ Paper 
and a SASCO System so as to ensure maximum flexibility 
and ease of entry-cham~e)o 
Mark II of this program is to be implemented on ~n 
I.CoT. 1903 with disc-file and multiple-access T.R.A.Co 
facility, but this is not expected to be operational 
till 1968. 
.ii. 
aor~ TITLE" (JEDT=4/P.eeASING SOaTl,,,=4_.S_.fT.. 
STREAM lie " i'NITI AL . INPU"T" 
ellll 
e1112 
11111 
11112 
11113 
11114 
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11213 
I1214 
11311 
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2/2/2 
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-LIMITATIONS 
ON .CANADIAN *COMMITMENTS. 
*ANY *NATION 
• MUST BE "CONCERNED 
THAT ITS *OBLIGATIONS' 
DO NOT *OUTRUN ITS *CAPABILITIESo 
A ,MIDDLE "POWER 
SUCH AS "CANADA 
~UST BE "PARTICULARLY "CAREFUL 
TO ,RATION ITS "COMMITMENTS. 
• ALTHOUGH AT THE *END÷OF÷THE÷WAR 
• CANADA *COULD÷HAVE÷DEVELOPED 
THE "CAPABILITY 
TO ,MANUFACTURE *NUCLEAR÷WEAPONS 
IT ,ELECTED 
AS A MATTER OF "DELIBERATE *CHOICE 
• NOT TO "BECOME 
A *NUCLEAR *POWERo 
-ALSO. "CANADA 
DID *NOT *BECOME÷A÷PARTY 
TO THE "INTER "AMERICAN 
• DEFENCE "SYSTEM. 
• AND. AT THE "CONCLUSION 
OF THE "KOREAN *WAR 
-CANADA "WITHDREW 
HER "TROOPS FROM "THAT÷AREA. 
21311 
21312 
~LPPENDIX A (b) 
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. A project supported by the U.S. National Science 
Foundation at the University of Bloomington, 
Indiana, has just been started, to make a Thesaurus 
for Information Retrieval in 50 languages. 
Also a historical Thesaurus of English is being 
compiled on a long-term basis by Professor Samuel 
at the University of Glasgow; and another, compiled 
by John Bromwich, is being put on magnetic tape at 
the Linguistics Computation Centre, Cambridge 
University. 
The properties and structure of thesauruses and/or 
conceptual dictionaries have never yet, however, 
been mechanically examined; partly because, until 
lately, machines with rapld-access-time to suffic- 
iently large memories were not available, and 
partly because of the overall cost of such a project. 

Lance & Machines, p.114. The Report gives this 
brilliant technical achievement just 3 sentenees 
on p.114, and ~ppears not to know of the fact that 
a mechanical justifier using a logic and working 
up to 95% accuracy is now in use on an actual news- 
paper (personal communication from Dolby & Resnikoff). 
.i. 
6. Lanamage & Machines, p.26. 
See also 
(See "Segmenting Natural Language by Articulatory 
Features" in the present Conference.) 
The phrasing method offers two operational 
simplifications 
i) by mapping the distribution of stresses on to 
a binary frame; 
il) by applying a phonetlcally-derived feature 
to Ear, instead of to syllables or phonemes. 

REFERENCES

LanguaKe and ~qhines: Computers in Translation 
.and Linguistics. Available from Printing & 
Publishing Office, National Academy of Sciences, 
2101 Constitution Avenue, Washington, D.C. 20418. 
Price ~4.00. 

Margaret Masterman, R.M. Needham & K. Sparck-Jones: 
The Analogy between Mechanical Translation and 
Library Re trieval.,(Proceedings of the International 
Conference on Scientific Informatiom, 1958), 
Washington, D.C., National Academy of Sciences, 1959, 
p.917. See also, on this analogy, 

Margaret Masterman: Translation, (Proceedings 
of the Aristotelian Society, 1959-60, P.79); 

R.M. Needham & I. Joyce: The Thesaurus Approach 
to Information Retrieval, (American Documentation, 
Vol. 9, 1958, p.192). 

J.L. Dolby & H. Resnikoff: The English Word Speculum 
in 5 vols., (Lockheed Missiles &~pace Company, 
Sunnyvale, Cal.) 1964. 

On the Structure of Written English Words (Language 
Vol. 40, No. 2,) 1964. 

F. Krollmann, H.J. Schuck and U. Winkler: 
Production of Text-related Technical Glossaries 
bY DiK{tal Computer, (mimeo, undated) ; 

La Terminologie, Problemes de Coop@ration 
Int ernationale, 

Expose de M.J.A. Bachrach, Chef dm Bureau de 
Terminologie de la Haute Autorite de la C.E.C.A. 
a Luxembourg - (The Applied Linguistics Foundation) 
a Strasbourg - Maison de l'Europe, le 6 Septembre, 
1965. (mimeo). 

L~dia Hirschberg: _D_ict!onnaires automatiques 
9our Traducteurs h umains, (Journal des Traduc- 
tours, Montreal, Vol. lO, No. 3 (1965), pp. 78-86. 

Lydia Hirschberg: Dictionnaires ' Automat iques 
MultilinAnAes, Conception, Utilisation, Realisation, 
(Colloque sur la Terminologie, Luxembourg, ler 
avril, 1966. Universit~ Libre de B ruxelles, Centre 
de Lingt~stique automatique appliqu6e). (mimeo). 

D. Shillan: Spoken English, Longmans, Green (Lon- 
don) 1954/65; 

D. Shillan: article in MET___~A (Montreal), Vol. XI, 
No. 3, 1966; 

D. Shillan: article in English Laruraa~e Teachin~ 
(Oxford), Vol. XXI, No. 2, 1967. 

Calvin Mopers: T.R.A.C.~ A procedure Des cribin~ 
LanKua~e for the Reactive Typewriter (Vol.9 No.3 
i966, Communications of the A.C.M.) 

R. McKinnon-Wood,& D.S. Linney: T.R.A.C. (Vol.2 
of Report to O.S.T.I. on Automatic Syntax 1966) 

Margaret Masterman: Semantic Algorithms (Las 
Vegas Conference on Computer-related Semantics, 
1965) 

E_veryman' s French-English English-French Diction- 
ary with spe¢ia! ~reference to Canada. compiled 
by Jean-Paul Vinay, Pierre Daviault, Henry 
Alexander, (Dent & Sons, 1962) P.494. 
