CHALLENGE PAPER: HOMEOSEMY —ON THE LINGUISTICS OF INFORMATION RETRIEVAL*
Hans Karlgren
KVAL Institute for Information

Science

A Need and a Tool?
[1]

Linguistics is necessary f o r
retrieval systems.

the design of future computer-based

information

This is a strong claim. Not surprisingly, several documentalists take offense when linguists
propose it. They f i n d that the present design: of retrieval systems is not fundamentally
deficient and needs polishing rather than remaking, a n d / o r they expect major contributions
to come f r o m people w i t h i n the field rather than f r o m more or less ignorant outsiders.
Insistence on a linguistic approach is often mistaken f o r an argument f o r use o f natural
language. But our claim is more fundamental. We shall make [ 1 ] stronger by adding[2]

Linguistics in i n f o r m a t i o n science is not restricted to possible natural language
processing.

In the f o l l o w i n g we shall take f o r granted that the reader shares our view
[3]

that

mechanical retrieval systems as known today are very remote f r o m what they could
become in a foreseeable future,and that[4]

the major restriction today is not in the amount of retrievable data, the availabilityof the service, or the f a m i l i a r i t y therewith among users, but in the selectivity o f thesearch methods.

On the contrary, i.he development so far has increased the amounts of data accessible ascompared to the pre-computer period while something rather blunt has replaced an
extraction procedure which was often, thanks to qualified and dedicated librarians, highly
selective and adaptable.

*This paper was formulated as a challenge paper to guide the discussions. Admittedly disputable statements arepreceded by bracketed numbers.

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Hans Karlgren
Consequently,[5]

research and experimentation should be redirected from mass processing toprocedure testing, from building up still more data bases to be processed byconventional methods -- the strengths and weakness of which are well-known, tothe study of small amounts of materials with more complex and less known
retrieval procedures.

Accordingly,[6]building new data bases, installing new enquiry terminals, or training users in the
manipulation of existing systems should not be tolerated, unless such measures canbe justified on the grounfc of their immediate profits without reference to possible
research merits.

This view is in agreement with the principle of transferring research resources from areasof highly predictable results to such as we know less of. If a few large-scale but predictabledata-base projects per year could be eliminated, the research on crucial retrieval problems
could presumably be easily financed.
This is an argument for long-range planning of this kind of research. A significant
improvement of performance can certainly be achieved with any technology by making
users more accustomed to the characteristics of that technology so that they play the gamewell. Thus, if users are taught to modify their habits of asking and of writing headings orsummaries, etc., existing systems will produce better results. However, knowing how little
we know about search methods and realizing how short our experience of mechanicalinformation processing is altogether,[7]

we shall normally reject every proposal for improving the efficiency of systems
essentially by changing the habits of the users on this account.

It is a good question what change of habits is essential.
The reasons for [ 7 ] are, among others, that[8]

the improvement trend produced by such means cannot be extrapolated beyond aceiling because there exist inherent limitations,

and
[9]

such a palliative treatment may be as disastrous as symptom-suppressing drugs
administered to a person who is seriously ill; the temporary success may mask theneed for long-range research.

Instead, the users' reluctance to comply with the formats prescribed by a system should betreated as a healthy reaction and be studied as such and not taught away.

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Homeosemy--On the Linguistics of Information Retrieval

It might be tempting to specify as a target level of ambition
[10]

that

a mechanical search system should perform as well on large data sets as a qualified
and well-informed human does on small sets,

and that[11]

the gain of mechanization should be wider coverage and faster inclusion o f newmaterial (learning), particularly important in new interdisciplinary fields which are
typically those where human oracles f a i l .

But this level of ambition, while unattainable in some respects, is probably too low inothers. Our own experience f r o m retrieval of passages f r o m w i t h i n a single text - - say as
little as 10,000 words of legal text - - reveals that, even so, humans f a i l to extract all
relevant items. It is almost impossible to f i n d all relevant i m p l i c i t cross references to agiven passage. He who has at his finger-tips all passages f r o m the Bible, or a set of legal
codes, or a normally ill-structured computer reference manual may pass as an uncommonly
learned man:[12]

document (passage) retrieval may be n o n - t r i v i a l even f o r humans when the data set
is very small.

Now, granted that there is a need for some fundamental new insights in i n f o r m a t i o nretrieval, it is still not obvious that the linguists are those who could supply the missing
tool. If documentalists accept good human performance as a challenge level there may besome aprioristic reasons for expecting linguists to have something to say; they might be
expected to know a little about how humans do it. But we need more evidence than t h a t
The linguists, clearly, carry the burden of the proof for statement [ 1 ] .
Let us, already at this stage, eliminate a possible compromise stating that the kind o f"linguistics" necessary f o r these tasks is a general science about Language, including suchstudy of formal languages as mathematical i n f o r m a t i o n theory or f o r m a l logic. We do notmean merely that i n f o r m a t i o n science and linguistics unite in the most abstract spheres.
Our issue is not whether this over-all study should be labelled linguistics or semiotics orinformatics. By linguistics we mean the kind of knowledge peculiar to linguists, i.e.,knowledge about certain properties of natural languages.
The Retrieval Problem
One can view document and other retrieval systems as a kind of question answering
devices*.
The request can be understood as " D o you have something like X X X ? " and the elicitedoffer may be understood as "yes, I have Y Y Y " , or "In a way. I do have Y Y Y . " We shallloosely say that the answer is identical to the question i f X X X = Y Y Y .
•It is true that the "request" for literature of a given kind may also be understood as a command, but thatinterpretation does not preclude the question status of a search question, since all questions can be described ashaving the deeper structure of a command tc supply information.

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Hans Karlgren
In an effort to distinguish between document retrieval, information retrieval in general,and other kinds of question-answering systems, we have found it useful to consider thefollowing three kinds of question-answering systems, assuming the system at any one point
in time to be deterministic in the sense that it produces only and all the same answers to
any one given question.
We have:order i:systems with a finite set of questions

order ii:

systems with a finite set of answers

order iii: systems with an infinite set of answers.
Order i. This group contains systems like those for airplane booking or spare parts
inventories. The possible questions are many, the usefulness of the system enormous, andthe problems of design and implementation may be formidable in several aspects, but one
thing about them is trivial: the relation between question and answers. The questions are allforeseen. The answers could in principle be assigned a priori to the questions. AH
refinements over mere listing could be summarized as storage technique.*
Order ii. This is the typical document retrieval system. Whatever the question, the answer
is a list of document references. The set of possible answers is the set of all subsets of theset of document references. The possible answers are many but in principle known prior to
the questions.
Order iii. In the general question-answering system, the answers are derived on the basis ofanalysis of given input statements. The system can do more than reproduce statements thathave been given to it. These are the systems which are expected to say whether a given
substance will resist a certain load, whether a transaction is compatible with a given
contract and a given set of legal rules, etc., etc. At this point, we are deeply into artificial
intelligence and information processing in general.
Now, the distinction between finite-infinite may be impressive in definitions, but systems
designers derive little comfort from the finiteness of very large numbers. Very large setsare often better treated as though they were infinite. To avoid in this field a repetition of
the long fruitless discussions in linguistics about the finiteness of the set of sentences, ourtentative definitions above should possibly be amended so that the crucial fact is whether
or not the designer can make use of the finiteness.
Another consideration which blurs the nice distinctions above is the need for interactive
operation as soon as the relation between question and answer becomes at all complex.
Thus, a document retrieval system aiming at selecting a subset of a given finite document
file may need to enter into a dialogue with the questioner. In addition to straightforward
answers such as "No, we do not have that" or "Yes, we have the following suggestions..."there are other adequate responses. In fact, we will very soon find almost the same wide
range of responses to a question as in other studies of the semantics and pragmatics ofquestions. The system may, in one disguise or another, produce replies such as "Thequestion is unintelligible", "Yes, but that would be at least 153 000 items; do you really•Text compacting is a fascinating field in itself and does have a bearing on linguistics. But here we are rather
in the domain common to linguists and others who study code design.

170Challenge Paper: Homeosemy--On the Linguistics of Information Retrieval
want them listed?", "What do you mean by XXX?" or "Do you understand 'XXX* as 'YYY
without ZZZZ'?", "Your question cannot be answered as it stands but do you object if we
replace it by QQQ?", etc., etc. This meta-dialog may contain a potentially infinite numberof systems responses, even if the number of ultimate answers is finite.
Still hoping that our distinction is of some value we shall concentrate on order ii and weshall restrain the term information retrieval to this case of information found and lost
In document retrieval, the crucial problem - - note the singular form - - is to match thequestion/request with the description stored about the document. This problem does notessentially- change its character if the description is of one kind or another; the full text ofthe document is just a special case of a description.
If the items to be retrieved are not documents but, say, persons, patents, precedence cases,
chemical substances, or processing methods, the retrieval must nevertheless operate on
descriptions defining what can be offered.
[13]

Thus, in any retrieval system for every retrievable item there must be an internallyassigned description, an offer.

[14]

The general retrieval problem is to match requests with offers.

[15]

The definition of a good match is far from trivial. We need less prejudiced criteria
for a good match.

[16]

In particular, the definition of matches and the evaluation of goodness of fit must
be independent of proposed search algorithms.

The actual design of retrieval systems requires much more than a good solution to problem[13], but that is the problem which distinguishes retrieval design from other difficult
systems engineering tasks.
Much has been written about the form and rnanipulability of descriptions. Perhaps too
little attention has been given to the contents of the description. The adequacy of thedescription and the agreement between request and offer are different but related matters.
The former concerns the relation between the object to be retrieved and its description, thelatter the relation of that description to a request
These two relations have been particularly confused when the object to be retrieved is itself
a text*, as is the case in document retrieval, which therefore is a very special case. And it isclearly one for linguists to handle whether or not they are in retrieval systems design. It isnecessary to have philological knowledge about the relation heading/body of text, etc. And
there are essential linguistic problems in the definition of the topic of a text; this has to dowith theme/rheme relations, concepts which are defined on the sentence level but not yet
on 'text' level.

•Obviously, a (ext can be described like any object by extraneous properties; one would be glad to have in ascientific system such features as "original," "elementary," "echo-paper", etc. The case which is often tacitly
assumed to be the only relevant ore is when only the contents of the text itself are described.

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Harrs Karlgren
Therefore,[17]

We recommend a study of the relation between document text and document
description to be made independently of algorithms for deriving a description froma text. Just as it may be fruitful to investigate what constitutes a good match
without considering how the pair is found, it may be worth-while to investigate
what is a good description for a given purpose without considering how it isobtained.

The book, Linguistics and Information Science, by Sparck Jones and Kay (1973) seems tolead up to the conclusion that systems of order ii could better be handled as special cases ofsystems of order iii. The restriction to prefabricated answers does not make the task
essentially easier. Conversely, one could maintain that even much more powerful questionanswering systems could and should be designed as systems with retrieval components. Evena specially generated answer, individually phrased for the client, may be a simple functionof partial answers retrieved from a set of input items (elementary statements or "facts").
Concluding,[18]

an unprejudiced study of questions and answers is crucial,and, in particular,[19]

the role of presuppositions must be clarified.

On the one hand, it is clear that an information retrieval attempt may have as one of its
major and intended results a reformulation of the question, based on the wider knowledge
contained in the system (and not only in the documents themselves, if it is a document
retrieval system). There must be a means for the answering system to reject presuppositions
in a question without rejecting the question altogether.
On the other hand, a questioner must be able to use a system even though he does not
accept all the presuppositions built into the descriptions (which may have been phrased along time in advance). It must be possible to come around moderate shifts in perspective, if
we want the system to age slowly. This problem area deserves study in the light of themodern study of presuppositions.
A Non-Linguistic Approach[20]

Irrespective of their various algorithms, it seems that most techniques practised
today are based on the following conception.

Request and offer are both phrased or rephrased in a retrieval language in which thematching (the "manipulation") is performed. (The "request language" and the "indexing
language" are here seen as subsets of one operation language, just as questions and answers
are subsets of one language; we disregard differences of format).

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Challenge Paper:

Homeosemy—On the Linguistics o f I n f o r m a t i o n Retrieval

Since the original request is not always given in the retrieval language it must be translated
into that language prior to searching and matching. Similarly, the original (document orother item) specification may have to be transformed into an offer expressed in the
retrieval language by a translation procedure often called indexing.
We then have the schema
R->R': the given
requestis t r a n s l a t e d
i n t o an e f f e c t i v erequestsusceptible tothe manipulationneeded f o rmatching

0->0': the given
offer

is translated
into an effective

offer

susceptible tothe manipulationneeded f o rmatching

Subsequent manipulations operate on the R' and the O' and lead to assignments of O's to
R's (or vice versa, i f you prefer).
The matching ofi.

Identityii.

PartialR's and O's are based on one or more o f

the f o l l o w i n g :identityi i i . Some logical calculus (typically: Boolean algebra).
Even when the computations are f a i r l y complex, consisting, say, o f establishing chains o fimplications, they break down the comparison, in a few steps, to an assessment of identity
or non-identity of primitives. These matching procedures in themselves are not regarded as
linguistic procedures.
Systems o f

this design often do include substantial linguistic components, such asi. Parsers or other tools f o r input analysis (automatic reformulation o f requests
a n d / o r "automatic indexing")
i i . Linguistics-inspired design of
The latterthe retrieval

language.

may include such "natural" features asii.i

word order as an expression o f semantic

ii.ii

modifier/modified

distinctions

relationsi i . i i i links and rolls, "cases", and other relations analogous to the semantic relationsin natural language.
But the crucial problem of matching, the retrieval problem, is not treated as a linguistic
problem. The underlying assumption is that once the request and offer have beentransferred to the exact f o r m which the retrieval language stipulates, they can f o r retrieval
purposes be considered as unambiguous. The rest is a jeu derencontres.

173

Hans Karlgren[21]With this attitude towards retrieval, the use of linguistics is optional.

The designer may or he may not permit requests and document descriptions to be written
in some more or less natural language. But, that is the idea, he may also impose severe
restrictions on the questioner and thereby eliminate as much as he choses of the linguistic
aspects and complications. Similarly, the designer may make the retrieval language more orless natural, but, and that is the implicit assumption, he is also free to define this internal
logical representation independently.
The need for identity on some level - - the R and O as wholes, parts of R and O, or
primitives contained in R and O — requires a control of the retrieval language. This
control is of a destructive kind and might in travesty of Reader's Digest's well-known
slogan be summarized under the exhortation Decrease Your Vocabulary (and Your Syntax).
The problem of variation in the expressions of R and of O when both mean the same oralmost the same thing is met by trying to eliminate that variation. There are two kinds ofsuch linguistic reduction:
i. Elimination of variation of expression where no difference in meaning exists:
standardization of usage. Standardization is sufficient only if the system is oforder i ("trivial" question-answering).
ii. Elimination of the variation of expression when the difference in meaning issmall. The control then means a compulsion to use the closest expression from apermitted set (sometimes, called a thesaurus although its prime property is not tobe rich but to be poor).
The translation from R to R' and from O to O' then requires an approximation procedure:
instead of saying what one means, one says something one does not exactly mean in thehope that the other person who is also not saying exactly what he means will have said
exactly the same.
[22]

One could summarize the use of vocabulary control and other language control inorder ii systems as an attempt to increase the probability of rencontre by reducing
precision.

Precision is here taken in its technical meaning of degree of specification. The result willbe, and equally so whether the restrictions apply to R and O or only to R' and O', thatcertain information will never be used and that the decision never to use it has to be madeprior to searching. Whenever such decisions prove to have been premature, the selectivityof the system will be impaired.
We conclude:[23]

Language control which implies reduced precision of input data is an adequatemeans of eliminating chronically irrelevant information but is no general solution
to the matching problem,

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Challenge Paper:

Homeosemy—On the Linguistics of Information Retrieval

because, and this is a major[24]

point,

in the general order ii case, it is impossible to know what i n f o r m a t i o n to disregard
in a request or an offer until one has seen the partner of the match.

We need to make abstractions but we never know a priori which is the best direction to
abstract into. A restricted language, to the extent it is restricted, forces us to make a priori
decisions on what to discard. Thus, a description ( A , B, C) in a simple system with a f i n i t e
set of descriptors leaves undecided whether this item is a special case of A B or of A C or o f
BC; any one of the descriptors may, in a t w o - o u t - o f - t h r e e match, be disregarded. But thesystem did impose on the indexer the choice of exactly A in place of whatever near-A was
originally given.
A Linguistic

Approach

The Dream of the Ideal
Language.
The approach we called non-linguistic wascharacterized by an effort to replace natural language by a universal exact andunambiguous representation on which a calculus could be defined. Linguists smile sadly at
the new proposals for exact logical representaiions of the meaning of natural language
texts. The dream of the ideal language spurred many ambitious attempts over the centuries,
since the 17th century, i f not earlier. A l l thc:'se great men with their fantastic systems
failed, certainly not f r o m lack of time, zeal, or genius. No modern systems engineer shouldtake it as a personal distrust when his linguist friends tell him to give up as a bad job his
design for an exact over-all representation, be it a general-purpose classification of allconcepts or something else. The linguists react to proposed ideal languages more or less like
physicists do when presented with another proposal for a perpetuum mobile; it is not thatthey would not like to have one. But there is overwhelming empirical and theoreticalevidence that a rigid but yet inclusive language will never be designed:[25]

A

universal linguistic perpetuum

immobile is notpossible.

Reasonably, then,[26]

The design of exact logical representation of knowledge, except for narrowly
restricted highly specialized domains, should be encouraged no more than should
perpetuum mobile construction.

Hopefully, we need not take any stand on the evasive philosophical[27]

issue ofwhether

The meaning of an utterance in natural language can in principle be specified interms of a f i n i t e set of semantic primitives and well-defined functions thereof.

Personally I think that [ 2 7 ] is exceedingly implausible, but even those linguists who dosupport it will presumably agree that the semantic representation postulated there issomething much more complex and explicit than any representation which could beconsidered for retrieval purposes

175

Hans Karlgren
Exact Meaning Representation Difficult.
Special purpose codes can, of course, be invented
and have been invented f o r particular applications. Thus a retrieval system for chemical
substances may work satisfactorily i f the substances are specified with some chemical
formula. But as soon as the retrieval questions expected are permitted to refer tounconventional procedures f o r chemical procedures or non-listed families of substances, werisk exceeding the scope of such an exact special-purpose language.
Even assuming that a sufficiently inclusive exact representation were f o u n d f o r a given
purpose, there are other obstacles to the nonlinguistic approach:[28]

It is inconvenient f o rhumans to write and read in a f o r m a llanguage.

For evidence of this statement, it should suffice to refer the reader to his own bitter
experience. Even moderately complex formal systems create enormous amounts o fbrainpain - - and errors, and that among formally trained persons, too. Even, Boolean
expressions get out of hand when the levels rise beyond three or four. A n d professional
programmers are haunted by formal errors in programs.
This is not a plea f o r using natural language under all circumstances. A r t i f i c i a l languages
could and should be improved. To me, it seems evident that the many millenia o fexperience built into the structure of natural languages should then be resorted to. The
fatal point is that i f a language, artificial or not, has enough "natural" features to be
attractive to human users, it is likely to become inexact and ambiguous.
Thus, one major convenience feature is what might be called redundancy adaptation: a good
margin where mistakes are likely to appear and reduced expressions elsewhere. This
human-oriented feature necessarily makes the texts at least locally ambiguous.
Similarly, the semantic f l e x i b i l i t y , which is probablyvagueness; we shall come back to this point.

necessary, is liable toproduce

A less obvious obstacle to inducing humans to produce formalized output is that they often
f a i l even when their performance is formally correct. Humans users tend to introduce
unintentional "natural" features. Stretching the meaning of exact to something like "having
the well-defined meaning which can be derived f r o m the specification of the language, andnothing but that meaning", we could even put it
[29]

An

unambiguous and exact man-made text does not exist.

What I am trying to say is that one may well be cheating oneself into believing that R andO were more formally specified than then really are. The writer and any human reader w i l lstill read into the text information which, according to the definitions, of the language, is
not there, and which w i l l be ignored by the system.** l f we look at all such permutations of the statements of a Fortran program as arc equivalent tc the computer,
only very few "make sense". On the other hand, quite a few moderately wrong programs do make sense to ahuman reader, who can correct them without real effort. Similarly, a mathematical proof, say. of Pythagoras'
theorem, might still be a valid proof, after shuffling some of the lines, but no reader will be able to see thepoint.

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Challenge Paper: Homeosemy—On the Linguistics of Information Retrieval
Man-made texts seem to have a text-structure, presuppositional restrictions etc., whatever
the a priori norm says about those.
A very simple illustration. The Boolean (sub-)expression "A and not B" may well be
intended as "A without B". Now the meaning of 'A without B' is quite complex, as appears
from the fact that the offer of a document carrying the title
diesel engines without injectors
is an adequate response to questions such as
diesel engines with injectors
or eveninjectors for diesel engines.
For whoever wrote about diesel engines "without" injectors, presumably either explained
why such deprived engines are adequate or he makes the point that injectors are not, after
all, indispensible. In either case he says something important on the role of injectors indiesel engines.
The point is not that a retrieval language cannot do without without, but rather that wecannot be so sure we have eliminated it just because there are no other connectors thanAND, OR and NOT in the texts. Naturam furca expurgas...
Some convenience can be gained by using a less formal input language for R and O andtranslate it into an operational language. Now,
[30]

The translation of R and O, if written in a language which has enough of naturalfeatures to be attractive to human users, into an exact logical language will cause
substantial losses of information.

[31]

These translation losses will in general be unpredictable by the user, unless the two
languages are very close to each other.

We may note that exhaustive internal recoding of a given more or less informal text is a
far more advanced task than the admittedly difficult (mechanical or manual) translation
between natural languages, since in the latter case obscurities may (in fact: should) betransferred unresolved to the target text
Exact Representation Unnecessary. This may all seem defeatist. A slightly encouraging
observation, however, is that there is not necessarily a real need for a complete logicalanalysis of the requests and offer. We are interested in their mutual agreement rather thanin their explication. What we want to do, after all, is not to relate them to a system ofgeneral knowledge but to relate them to each other. We need to know how well O
approximates R rather than the absolute value of either.

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Hans Karlgren
In numerical work, we know exactly what "approximately" means. "Appr. 1.3" may be
defined as "not less than 1.25 and not more and less than 1.35" or as 1.3 plus/minus anerror which can be well defined, say 1 standard deviation. We need a theory for qualitative
approximation, explaining exactly what it means that an offer is Epproximately what was
asked for. In what way is silver an approximation of gold, of money, of chrome, and ofphotography?
We can compare with the mathematical problem of determining the common divisor of
two given numbers. We achieve this if we analyse each number by itself and check the two
results for common primes. A well-known algorithm, suggested by Euclid, produces thecommon divisor directly, by successive operations (divisions) on the two given numbers or
one of them and the result of the previous operation. We need Euclidean algorithms,
operating on the pairs of an R and an O and yielding (a measure of) what is common tothem. We need to establish not the meaning of one given expression but the similarity of
meaning between two given expressions. The fundamental concept, then, is not meaning,but similarity of meaning. We shall use the word homeosemy (from Greek homojos, almost
the same) for the similarity of meaning between two expressions.
Exact Representation Insufficient.
It is often taken for granted that given exact
representations, R' and 0 \ the agreement between these two will be trivial to define (if not
to find; long inference chains may present formidable computational problems). Basically,
it is assumed that similarity can always be reduced to partial similarity (or to very simple
logical relations; cf. supra).
We find no real support for such an assumption.
[32]

If we want to define a topology over the set of expressions rather than to explicate
each of them, we need not assume that the homeosemy of any two expressions or
expression components can be further reduced. Rather, our analysis should be built
up from the concept of distance or association between primitives ("elementary
homeosemy").

Homeosemy, then, becomes a quantitative concept as fundamental as inference or set
inclusion. It is in this perspective the work on associative word association should be seen.
Consider our attempt at a formal mathematical formulation of association with 'damped
transitivity' (Brodda and Karlgren, 1969).
I hesitated whether I should put as sub-heading for this section[33]

Exact representation is not desirable.

What I meant was that vagueness may be the price that has to be paid in order to achieve
the kind of gliding from one concept to another which is necessary for non-trivial
retrieval.
We note that in natural languages - - and their design is successfu' in this respect —communication normally proceeds without explicit definition of terms. Not only do
different persons attach slightly different meanings to the same terms but no person has
ever even 10 himself delimited an exact or definable meaning of terms, except possibly forsome few of them.

178Challenge Paper:[34]

Homeosemy—On the Linguistics of I n f o r m a t i o n Retrieval

In n o r n a l human communication, introduction of an explicit d e f i n i t i o n f o r natural
language terms is a symptom o f malfunction.

One may ask oneself whether natural language succeeds not in spite o f but thanks to theabsence o f rigid d e f i n i t i o n of meaning. The f l e x i b i l i t y of natural language semantics
appears also f r o m the observation that[35]

definitions o fterms age much faster than the terms themselves.

The Wilkins* 17th century classification o f elementary English terms in a systematicconceptual classification, proceeding f r o m divine, human, animal and so f o r t h , sounds veryancient. But the words themselves remain with approximately the same meaning and actual
texts f r o m the same period can be reasonably well understood today by those who know
Modern usage.
Although we must admit that we f i n d natural language more impressive the more we see o f
how it works, all this was not mentioned as an argument against a r t i f i c i a l languages but asa reason to build into any retrieval language some of this f l e x i b i l i t y . For this purpose,[36]

meaning differences between terms and meaning shifts should be studied,particularly the meaning shift between question and answer and the shift due tointroduction of new terms, in undefined vocabularies.

Here, we can draw on rich funds o f
Effects ofphilologicalknowledge.

Linguistics

So Far. The effects so far have been surprisingly meager, as is made evident by Sparck
Jones and Kay and by later publications. The linguistic designs which have been tested
have demonstrated little effect over straightforward non-linguistic methods. In some cases
the linguistic ingredient seems to have had a negative effect, even in matters of analysis ofnatural language i n p u t
Inprinciple,[37]

immediate practical tests of economy or over-all selectivity are not adequate for
evaluating new methodology.

Otherwise a polished p r i m i t i v e system w i l l almost always win over innovations which areless ripe for production. Practical mileage economy tests may be adequate for a Ford and a
Volvo but are uninteresting in a comparison of a Volvo and a prototype electric car.
Nevertheless, when some documentalists maintain that mere recording of (truncated) terms
without respect to word order or any other syntactical i n f o r m a t i o n performs better thanlinguistic analysis - - and when this happens to be true, with some qualifications - - it is achallenge.

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Hans Karlgren
One reason for poor success is that a kind of linguistic analysis which long ago (in somecases: centuries ago) had been given up within linguistics itself survives or is reborn indocumentalist applications. Thus, models equivalent to naive dependency or phrasestructure grammar have been allowed to represent linguistics as a science.
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These grammars which are nevertheless in some way elementary and fundamental,
cannot be used even as a first approximation to grammatical analysis. They may begood as grammar components, but a component cannot replace or approximate thewhole.

Thus, an innocent analysis of 'Electronical pedagogical equipment in nursery schools' andof 'Nursery schools with pedagogical equipment' will over-emphasize the differences. It
will find that different things are referred to - - gadgets and schools — and different things
are stated about them: where they are kept and how they are equipped. That kind ofanalysis will fail to see that two differences cancel out. The grammatical filter will remove
too much in such cases but yet not disclose the similarity of such pairs as 'Finland's export
to Sweden' and 'Sweden's import from Finland'.
Today.

Linguistics could immediately be helpfuli.

by dissuading documentalists from spending resources on vain attempts,
such asi.i

creating an ideal language,i.ii

attacking the general retrieval problem by means of language reduction,i.iii

trying to make their retrieval language more stable (by means of term
definitions or otherwise) instead of more flexible;ii.

by supplying

ii.i

professional tools for parsing and similar tasks,ii.ii

means for synonymy manipulation,

ii.iiiquantitative association methods for study of associative structures,ii.iv

ad hoc methods for grammatical filtering.

Since full-fledged analysis is probably not practical, algorithms must be designed on thebasis of the deeper insights about, sa>, transformational relationships. Computational
linguistics has lost its innocence and must draw some conclusions therefrom. One
conclusion is that since an expression in natural language can under various assumptions
yield so many reasonable interpretations, the procedure must take the uncertainty of any
analysis into account.

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Challenge Paper: Homeosemy—On the Linguistics of Information Retrieval
In the Future. Linguistics must be involved in the retrieval problems as such. These are
linguistic by nature; there is no choice whether or not to treat them in a linguistic manner.
Effects on Linguistics
More focus will be placed oni.

exact study of inexact expressions

ii.

study of shifts of meaningiii. study of question-answering
iv. semantic topology.
So far, emphasis has been placed on the binary distinction between the same and not thesame meaning. Of old, linguists have been keen on finding distinctions which wereotherwise overlooked. Lately, linguists have established equivalence classes of expressions
which have exactly the same meaning, (feeling very unhappy, some of them, when these'variants' turn out to differ after all, at least in theme/theme relations). Systematic study
must be made of the agreement between such expressions as cannot be treated as
semantically equivalent
References
Brodda, B., and Karlgren, K. "Synonyms and Synonyms of Synonyms". SMIL, 1969, 5, 317.
Sparck Jones, K., and Kay, M. Linguistics and Information Science. New York, Academic
Press, 1973.

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