PERSPECTIVE PAPER: QUANTITATIVE LINGUISTICS*
Wolf Moskovich
The Hebrew Uni\ersity of Jerusalem
Introduction
Qualitative analysis of language is based on the study of the opposition presence-absenceof a certain linguistic phenomenon in the structure of language without taking into
consideration the frequency of the phenomenon. Quantitative analysis, on the other hand,is a way of describing a linguistic system based on an estimate of the relative frequenciesof the phenomena under investigation. Quantitative linguistics (statistical linguistics,
linguistical statistics) is a part of modern linguistics "using statistical methods forinvestigation of acts of speech and the system of language" (Akhmanova, 1966, p. 219).
While analyzing the application of quantitative linguistics to information science, one has
to take into consideration all the three apices of the triangle: mathematical statistics,
linguistics, and information science. Representatives of all three disciplines are involved inresearch in the field, and their attitudes towards, and judgments of quantitative linguistics
are often influenced by their professional background. In the following discussion, weshall come across conflicting approaches to quantitative linguistics and its application to
information science, and our judgment will always be on the side of quantitative linguistics
as a sovereign linguistic field of study.
Though it is commonly accepted that quantitative linguistics is a part of mathematicallinguistics (or computational linguistics), every now and then there are attempts to exclude
it from these disciplines. This opposition usually comes from certain mathematicians who
consider mathematical linguistics to be a mathematical and not a linguistic discipline.
They reduce it to a study of deductive linguistic calculi or linguistic algorithms (e.g.,
Gladkij and Mel'cuk, 1970). They argue that statistical techniques are common for all
sciences and therefore their application to language analysis does not belong to linguistics.
There are other mathematicians who think that mathematical linguistics belongs both to
linguistics and to mathematics, being "a science^ that discovers and studies mathematicalstructures really existing in linguistic objects" (Srejder, 1975, p. 7). But the majority ofexperts agree that quantitative linguistics wholly belongs to mathematical linguistics.
"When we siudy the non-grammatical side of language, almost all of the problems
themselves are of a quantitative nature," writes Kiefer (1964). "No doubt, statisticallinguistics represents another approximation to language structure, complementary to thealgebraic one.
Statistical methods and theories are thereupon indispensable inmathematical linguistics" (p. 26).

•This paper is presented on behalf of KVAL Institute for Information Science.

57

W o l f Moskovich
In the book by Sparck Jones and Kay (1973), the terms quantitative linguistics,
linguisticalstatistics, and statisticallinguistics
do not appear; the whole camp of linguistics as
opposed to the camp of i n f o r m a t i o n science is referred to as linguistics,
theoreticallinguistics,
or computational
linguistics.
There is no chapter or section of the bookentitled quantitative linguistics.
Such a situation seems paradoxical since the authors claimthat the most substantial contribution linguistics made to information retrieval is in the useof vocabulary statistics. One cannot evade the impression that Sparck Jones and Kay
presented th« material in terms of direct application of statistical techniques to document
analysis without making it explicit whether the field belongs to linguistics or not. The
authors try in the f i r s t chapter of the book to set some boundaries between linguistics andi n f o r m a t i o n science being aware that "much of what must be explained in order to makei n f o r m a t i o n science amenable to computer techniques belongs to what linguists haveundertaken to explain f o r quite independent reasons" (p.3).
However, when it comes to the application of statistical techniques, described in Chapter 6,
Semantics, on more than 40 pages of the book, only one and a half pages (pp. 171-172) areallotted to linguistics proper (the section Automatic classificationin linguistics).
Another
section of the book, Statisticalsyntax (pp. 110-111), is not larger - - less than a f u l l page.
The whole plan of the book and the discussion of the relevant material makes it clear that
here the reader has to do with terminological inconsistency on the part of the authors
rather than with conscious distortion o f the picture.
It is a serious drawback o f the book that it adequately describes only work done in theUSA, Britain and France. Research performed outside these countries is mentioned only inpassing ( i f mentioned at all). The corresponding publications in other countries were notanalyzed, and as a rule, references are made only to their English abstracts taken f r o m
Abstract Journal: Informatics.
Consequently, the conclusions of the book are based only
on a partial survey of work done in the field. The book, furthermore, gives little attention
to theoretical and linguistically-oriented research in the field of our concern. It is as
though no valid research independent of i n f o r m a t i o n science applications has been done inquantitative linguistics.
In order to give an account of the contribution of quantitative linguistics to i n f o r m a t i o nscience, we need to evaluate current work in the field f r o m the point of view o fquantitative linguistics. The questions which are to be examined are the following: T o what
extent does application of statistical techniques to text analysis belong to linguistics and towhat extent to i n f o r m a t i o n science? What is in fact the current contribution o fquantitative linguistics to i n f o r m a t i o n science and what may be this contribution in the
future? What are the main lines of research in quantiiative linguistics, what arc thescientific results achieved, and what is the relevance of these results to i n f o r m a t i o nscience?
We shall try to answer these questions, not by relying mainly on the publications reviewed
in the Sparck Jones and Kay book but by taking into consideration a wider range ofliterature belonging to the field of quantiiative linguistics with due attention to original
work produced both in western countries and in the Soviet U n i o n .
The Interrelation of Quantitative

Linguistics and Information

Science

Information science deals with storage, retrieval and transmission of i n f o r m a t i o n . As atheoretical discipline it studies the rules and laws according to which semantic i n f o r m a t i o n
is created and transformed and here it shares its interests with theoreiical linguistics. As an58

Perspective Paper: Quantitative Linguistics
applied field it uses various methods of handling information, both developed within its
limits for its specific needs and borrowed from adjoining sciences. The more sophisticatedand complex the techniques of adjoining sciences are, the more difficult is theirapplication.
Quantitative characteristics of messages are studied by methods ofmathematical statistics known to every system analyst, and it seems only natural to usethem for information retrieval purposes.
Starting from H. P. Luhn's first successful experiments in automatic statistical indexing andabstracting in the late 1950s, statistical techniques were taken up by a number of
researchers in information science. Most of the systems created are based on ad hoc
techniques, and although the results obtained are as a rule satisfactory and encouraging,usually no qualitative analysis of the material subjected to counting is given.
It is noteworthy that many of the contributors to the 1964 Washington Symposiumon Statistical Association Methods for Mechanized Documentation, which can be
regarded as the culmination of the period of enthusiasm for the new approaches
to the central problem of document description by statistical techniques, are nolonger engaged in research in this area... The need to test bright ideas, whencombined with a growing awareness of the complexity of a retrieval system, andhigher standards of experimentation, brought research workers up against theprospect of long hard labor with a very uncertain outcome (Sparck Jones and Kay,
1973, p. 12)
Apparently there is a certain limit beyond which the brute force statistical approach cannot
penetrate, and additional techniques and criteria of a qualitative nature are needed.
The differences between the use of statistical techniques in information science andquantitative linguistics are twofold: (1) Quantitaiive information science usually relies on
ad hoc techniques, whereas qualitative linguistics tries to develop a general theory or modelof linguistic behaviour, and interprets its findings in the light of this theory (or model).
(2) Quantitative information science usually is satisfied with a pragmatic result and does
not study the qualitative characteristics of counted objects; quantitative linguistics compares
quantitative characteristics of counted linguistic units with their qualitative characteristics
and tries to find correspondences between them.
Although statistical techniques implemented in information science and linguistics may be
the same, the aims of the two disciplines are different. These differences should be a main
factor in defining the scope of quantitative linguistics and its potential contribution to
information science. The aim of quantitative linguistics is to find and describe the laws
governing the statistical organization of texts, and to discover the structure of language
through quantitative analysis of the behaviour of linguistic units in texts. The aim of
information science is to apply statistical techniques for document analysis, storage andretrieval in order to build workable information systems.
It is clear that information science can benefit from using the experience of quantitative
linguistics, and that statistical regularities of language behaviour discovered and describedin quantitaiive linguistics may serve as a basis for application in information science. It
goes without snying that some refinements of the statistical procedures created within
information science for its specific needs, as well as some facts relating to the statisticalcharacteristics of text units discovered within information science may be useful for
research in quantitative linguistics.

59

Wolf Moskovich
The State of the Art of Quantitative

Linguistics

According to the classification by Karlgren (1975a), the quantitative models so far usedwithin linguistics are divided into three main groups, as they try 10 provide
1. Quantitative arguments for qualitative issues
2. Quantitative descriptions of language phenomena
3. Quantitative explanations of language phenomena.
As an example of the model of the first group, a statistical procedure for determining thesequence of various themes in a text is given (Karlgren, 1975b). A computation based on a
statistical model, according to which the probability for recurrence of a word ischaracteristically greater when the theme is the same, suggests tentative demarcations
between text sections. But the hypothesis put forward - - that there is a borderline at thatand that point in the text - - is not a quantitative statement. The model is used to produce
and corroborate a qualitative hypothesis.
Examples of the models of the second group are the following: word frequency
distributions, quantitative descriptions of sentence length, syntactic complexity, semantic
uniformity of texts, semantic distances between linguistic units.
Such quantitativedescriptions are aids for determining qualitative issues, but they have also an intrinsic
interest
Models of the third group explain the language mechanism in quantitative terms. For
example: the relation between frequency and length of words is explained in terms of amathematical model to minimize communicative effort
Most of the research in quantitative linguistics potentially useful for information sciencebelongs to the second group of models in this classification.
Nowadays, frequency
dictionaries and concordances to texts in various languages are being produced on a massscale in line with the trend of recent years. The use of computers for their compilation
facilitates the work, and makes it possible to compute a larger number of quantitative
characteristics of linguistic units. For example, the statistical data given in the frequency
dictionary of present-day American English by Kucera and Francis (1967) include thefrequency of words, the number of genres and the number of samples in which they haveoccurred, word-frequency distributions within various subsets of the corpus, information
about type-token ratios in the corpus and various subsets, and word length and sentencelength statistics.
The advance of computers, as well as inner forces of development within linguistics
brought two major tendencies into modern quantitative linguistics: (1) an increased
attention to the analysis of subsets of natural language; a i d (2) a shift from statistics ofisolated words to statistics of word combinations, [loth tendencies have major implications
for the further cooperation between quantitative linguistics and information science.
Statistical analysis of subsets of natural language provides quantitative data and special
frequency vocabularies of narrow scientific and technological fields which can be
immediately used in information retrieval.
The former orientation of quantitative
linguistics towards the analysis of texts of general and literary character was of much less
(if any) interest for information retrieval. For the statistical study of word combinations,60Perspective Paper:

Quantitative Linguistics

the traditional statistical apparatus used in counting isolated words was not suitable. A new
apparatus had to be used, and new results were then achieved with its help. It marked areal revolution in quantitative linguistics which thus can study statistical interactions o fwords in text and not just their frequency. It brought new perspectives into i n f o r m a t i o nscience because such statistical techniques and data provide access to associative term
structures which are of particular significance f o r i n f o r m a t i o n retrieval purposes.
A notion of sublanguage was put forward and a multitude of frequency dictionaries ofwords and word combinations was produced in recent years (at least f o r the USSR, wheremore than 100 such dictionaries were compiled in the past 10 years, a complete list ispresented in Moskovich, 1967-1974).
For each sublanguage the f o l l o w i n g statisticalmaterials can be, and in some instances partly are, compiled ( N is the number of typicalsubcorpuses f o r a sublanguage):1.

N rank frequency dictionaries o f wordforms.

2.

N alphabetical

3.

General rank frequency dictionary of word forms (indicating f o r each word f o r m
and for each of its represenlations with a definite marknote: total frequency, total
ratio frequency, the number of subcorpuses, frequencies - - absolute and ratio - - inthese subcorpuses).

frequency dictionaries of word

4.

General frequency dictionary o f word forms.

5.

N frequency-distribution

6.

General frequency-distribution

forms.

tables.
tables f o r

word

7-12. Analogous inventories f o rlexemes.

13. N tables for

distribution.

sentence-length

14. General table f o r sentence-length
15. N frequency dictionaries of

distribution.

binary word

16. General frequency dictionary offorms.

binarycombinations.
word-combinations.

17. Alphabetical dictionaries of lexical-syntagmatic word distributions based on
semantic roles of the word in the sentence (this dictionary consists of two parts:
distributions o f the government words and distributions of the subordinate words).
18. N alphabetical frequency dictionaries of complete lexical-semantic complexes w i t ha given nucleus (both binary and more than binary).
19. General alphabeticalfrequency dictionary o f

20. N frequency-distribution
21. Generaltables f o rtable of complex-frequency

22. N frequency

complexes.

complexes.

distribution.

lists of semantic svntagmatic

23. General frequency

lexical-semantic

lexical-semantic

relations.

list of semantic syntagmatic

relations.

24. Dictionary indicating the semantic-relational productivity of lexemes (measured by
the number of different binary combinations which the given word enters).
(Gorodeckij, 1972)61

W o l f MoskovichComputer compilation of most of these materials presupposes the use of pre-machine text
marking, and means a transition f r o m collection of mechanical contexts (concordances) tomore complex statistical descriptions of linguistic distribution based on real syntactic andsemantic relations. Three main types of markings may be used: (1) d i v i d i n g lines between
morphemes; (2) grammatical or semantic indicators o f word forms; (3) indicators o fsyntactic roles and relations.
On the basis*of such preliminary markings (syntactic class, subclass, governing word, thetype of syntactic connection between words), an automatic grammar f o r texts of American
patent claims was built (Moskovich, 1966).
In one o f a number of similar studies, researchers tried to clarify the f o l l o w i n g question:
Do the semantics of the components and the type of syntactic links between themcondition the morphological structure of noun combinations in modern Ukrainian? For
the configuration N Nominative case and N Genitive case, when the governing component
designates various appliances and is characterized by suffixes -ac or -tor, the f o l l o w i n g
syntactic-semantic links between components of noun combinations are predicted: (1) tohave as an object of action — 70 per cent; (2) to be a part - - 20 per cent; (3) to be an
instrument - - 10 per cent (Skorohod'ko, 1964). The use of pre-machine text marking
broadens the possibilities f o r quantitative analysis and links it to other linguistic
techniques.
A completely different approach based on statistical discovery procedures of the k i n dusually applied in decipherment of messages was developed in recent years. The approachis conceived as an extension and perfection of classical techniques of distributive linguistic
analysis aimed at delimitation and classification of text units.
"Distributive-statisticalanalysis may be defined as analysis of text consisting of algorithmic procedures with a wideuse of statistics and based only on i n f o r m a t i o n about distribution in text of objectively
delimited text elements" (Ivanova and Sajkevic, 1970, p.79).
Distributive-statistical analysis can be applied to t i e delimitation and classification ofunits on various levels of language. On the basis of conditional probabilities of letters intext and in different positions in words, individual morphemes as well as classes ofmorphemes can be delimited. Several algorithms for distributive-statistical morphemic
analysis were successfully tested (Ivanova and Sajkevic, 1970; Andreeva, 1969).
More spectacular results were achieved in the application o f distributive-statisticaltechniques on the lexical-semantic level of language. The usual procedure is to compare
the theoretically expected and actual cooccurrence of terms in texts. The strength ofconnections among terms is determined by the value of deviation of their actual occurrencef r o m the theoretical one.
In such a way, semantic networks are built.
It can be shown t i a t the development and application of statistical association techniques
took place in linguistics and i n f o r m a t i o n science in a parallel fashion (Moskovich, 1972a).
Sparck Jones and Kay describe the history of research in this field as an event w i t h i n theconfines of i n f o r m a t i o n science. After reviewing relevant publications in a way that
implies that they belong to i n f o r m a t i o n science, they draw the f o l l o w i n g conclusion: "The
impact of all this on the main stream of linguistics is not discernible, and the impact on
linguistics of any kind has been slight. However, a few experiments have been performed
in which text based statistical associations have been exploited to obtain semantic
characterizations of words" (1973, p. 171). While this judgement may be supported by the
relative number of publications on the subject in information science and linguistics, it

62Perspective Paper: Quantitative Linguistics
hardly does justice to quantitative

linguistics.

The most prominent publication on statistical association techniques in i n f o r m a t i o n scienceis that of the proceedings of the symposium held in Washington in 1964 (Stevens et al.,1965). According to this book and other sources, statistical association techniques weref i r s t applied to the study of texts by psychologists. In a paper published in 1942, an
American psychologist, A. L. Baldwin (1942), used the values of the cooccurrence o f words
in the letters written by a female patient as indicators of corresponding connections amongideas in her m i n d .
The first attempts to use these techniques f o r i n f o r m a t i o n retrieval were made in the late
1950s (Needham, 1961; Doyle, 1959; G i u l i a n o and Jones, 1962). In linguistics, there was aparallel development.
In the late 1950s, several statistical procedures f o r measuring
syntagmatic and paradigmatic proximity of words were suggested and tested by Andreev(1959, 1961). A t the same time, Sajkevic (1961, 1963) conducted his experiments on the
discovery of semantic fields of language on the basis of statistical analysis of cooccurrenceof words in text. Cooccurrence of 1078 adjectives in a text sample of 2 m i l l i o n words ofEnglish poetry was analyzed. The interval in which cooccurrence was registered was a lineof poetic text (about 5-6 words). As a result of the analysis, paradigmatic groups of words
were discovered. These included synonyms {dumb - mute; weak - faint - feeble, vile base - mean, foul - filthy - loathsome; gentle - mild), antonyms (mortal - immortal; new old; great - small; dead - alive; strong - weak), and groups of words with similar meaning(green - fresh - new, lofty - divine - heavenly - sacred; heroic - brave - honourable noble).
A l l these links of words create a complex associative n e t
In later years, the ideas put forward by Andreev (1965, 1967, 1969) were mainly w developedin the direction of distributive-statistical analysis of morphology and those by Sajkevic in
the direction of lexical-semantic studies and their application to information science needs(Tritsker, 1964; Moskovich, 1965, 1969; Ivanova, 1967, 1969; Ivanova and Moskovich, 1968;
Sajkevic, 1970b). Particular attention in this latter set of papers was paid to the detectionof statistical associative links among words in various intervals of text.
The results
obtained o . large samples of text suggest that the most interesting linguistic results may beobtained in the minimal and medium intervals of urxt ( f r o m three words to a paragraph).
As a rule, in the minimal interval, syntagmatic relations are detected, whereas in themedium - both syntagmatic and paradigmatic ones are found.
First-generation andsecond-generation profiles of words were built, and it was shown that second-generation
profiles reflect paradigmatic links with more accuracy. The same distributive-statisticalprocedure was applied to translations of the same text into different languages, andimportant typological features of semantics of these languages were discovered. Textoriented specialized thesauri f o r various technical fields were created with a view to theirsubsequent application w i t h i n i n f o r m a t i o n retrieval systems.
One of the most serious projects on the use of distributive-statistical techniques both f o rlinguistic and information retrieval purposes was that of the Cambridge Language Research
Unit. In the experiments of this group, cooccurrence of words in the maximal text interval(i.e., the whole text) was counted and the possibility was shown of detecting groups ofwords similar to those found in Roget's Thesaurus of English Words and Phrases (Sparck
Jones, 1962, 1964).
Harper (1965, 1966) counted the cooccurrence of nouns in the interval of one sentence in asample of 120 thousands words of Russian texts on physics. Cooccurrence of every noun
with its governing and subordinate words was registered. Semantic distance between any
two nouns was defined as the ratio of the quantity of governing and subordinate words63

W o l f Moskovich
common f o r both of them, to the product of the freqiencies of these nouns. Semantic
groups of nouns were discovered which showed a good approximation to intuitively felt
groups of semantically related words. Similar experiments with Russian verbs are describedby Apresjan (1966,b). In some recent studies, cooccurrence of nouns with the same verbs as
subjects (or objects) was used as a measure by which nouns and verbs were grouped i n t oclasses. Results obtained showed good agreement with a classification of nouns and verbs
into semantic classes derived manually (Hirschman et al., 1975; Sager, 1975).
In the paper of Lewis et al. (1967), successful results of experiments on classification ofsynonyms and antonyms are reported. The authors used ten variations of formulae f o rmeasuring semantic distances. Three of these formulae were selected as giving the best
approximation to an ideal classification of synon>ms and antonyms.
Several attempts were made to build semantic networks on the basis of data on syntagmatic
distances between words in texts (Ratceva, 1965, 1966a,b). Along the same lines, researchon lexical collocations in text was performed.
It seems that a level of linguisticorganization distinct f r o m both syntax and semantics exists, since statistically significant
cooccurrences of words were found w i t h i n a span of three words (Bulaseva, 1969) and off o u r words (Zueva, 1970; Jones and Sinclair, 1972) on either side of the study words. A
number of such collocations display strong semantic cohesion.
A study of the notion of synonymy applicable both in linguistics and i n f o r m a t i o n retrieval
was undertaken by Brodda and Karlgren (1969).
Only a part of linguistic research in statistical association analysis relied on ideas andmethods developed by information scientists. An overview of the papers discussed aboveshows that almost all the research done in the USSR in the 1960s was initially independentof everts in i n f o r m a t i o n science. This is explained by the fact that in those years advanced
computers were relatively scarce in that part of the world, and statistical associativetechniques could not have been used successfully on a large scale. In the USSR, there wasalmost no independent research on statistical association techniques in i n f o r m a t i o n sciencein the 1960s. A l l the ideas and experience came f r o m quantitative linguistics. Actualmachine experimentation for information retrieval purposes was started recently (Borodin
and Kozokina, 1971).
In the West, the situation was apparently different; pragmatic
applications came first. But there is good evidence that at least part of that research waspurely linguistic and that another part had f r o m the start both information retrieval andlinguistic aims.
Distributive-statistical text analysis as an objective method for detecting lexico-semantic
links of words has great significance f o r modern computational linguistics, which relies
heavily on semantic networks and dictionaries. In a special linguistic study (Moskovich,
1971, 1972b), the heuristic potential of three methods of detecting lexico-semantic links o fwords was tested: (1) distributive-statistical text analysis; (2) psycholinguistic word
association analysis; \3) analysis of word definitions. Comparison of the results obtainedby application of these methods brought the author to the conclusion that the most detailed
and exact associative profiles of words are obtained with the help of the distributivestatistical method.
Such profiles reflect the character of texts chosen for analysis.
A specific part of i n f o r m a t i o n science dealing with automatic synthesis of words (which
are to serve as trademarks) and automatic retrieval of words-trademarks benefited f r o m
research in quantitative phonology. Corresponding i n f o r m a t i o n systems f o r processing
trademarks were often devised by linguists and based on preliminary research of the laws

64Perspective Paper: Quantitative Linguisticsof phoneme combinatorics (Brodda and Karlgren, 1964; Moskovich, 1968). As was shownin these studies, combinatorial properties of phonemes depend upon their acousticaldistinctive features, and phonemic proximity of trademarks was measured on the basis of
the distinctive features of the phonemes composing the trademarks (Moskovich, 1968;
Muljacic, 1967).
Research in statistical extraction techniques for automatic abstracting and indexing wascomparatively independent of preliminary linguistic work, although some of the systems
were designed by professional linguists. The method of automatic statistical abstracting of
Agraev et al. (1963) is based on the evaluation of the semantic weight of a sentence whichis regarded as a coefficient of connection of the analyzed sentence with the whole text ofthe document. The coefficient of connection is calculated according to a special formula,where the frequency of occurrence of words of the analyzed sentence in the whole text, thelength of the text and the length of the sentence are the variables. Another method ofstatistical abstracting takes into consideration only the frequency of terms in the text andthe number of words (terms and non-terms) in it (Purto, 1961).
Methods of statistical indexing which are usually based either on relative frequencies ofwords in documents or on the comparison of the frequency of words in a document with
their frequency in the whole collection of documents are similar to those used inlinguostatistics for defining themes of a text (Guiraud, 1960). Iker (1974, 1975) devised
and tested a computer system, SELECT, which isolates the major themes of a text byselecting those words of the text which are most correlated with all other words and the setof most frequent words (excluding function words) and using them as input to a factor
analysis.
For various applications, it is essential to know laws governing the distribution of words intext. Most of the relevant research in this area was done within quantitative linguistics notby linguists, but by expert mathematicians. The law of rank distribution of words known
under many names as the law of Zipf, Bradford, Lotka, Estoup, Mandelbrot, or Wyllys has
been discussed in many publications. Some of authors consider this law neither a linguistic
nor a mathematical one (Herdan, 1960, 1964); others point to its correspondence to facts ofdifferent language: (Sambor, 1969; Orlov, 1970; Arapov et al. (1975a,b). Studies of
distributions of linguistic units in texts showed that the character of their distributions is
not constant and varies according to the interval of text in which distribution is measured(Sajkevic, 1970a). However, for practical reasons it is accepted that words of medium andlow frequency are distributed according to Poisson's law. Measures for evaluating thestrength of connections in distributive-statistical association nets are sometimes based on
this assumption (Sajkevic, 1963).
Frequencies of linguistic units in text reflect their roles and properties in the structure oflanguage. A notion of productivity of linguistic units was suggested in (Moskovich, 1969).
The productivity of a linguistic unit is defined as its importance and role in the structureof language. The productivity of words can be measured by the quantity of its meanings,
quantity of its derivatives and quantity of set expressions with this word as one of theelements. Frequency of words strongly correlates with their productivity. The position ofan element within linguistic structure is reflected in its frequency (Moskovich, 1969). This
rule applies not only to the lexico-semantic level of language, but t:> the morphological one
as well. Productivity of an affix is defined as the quantity of words-derivatives with thisaffix in a language. Recent investigations showed that there is a striking correlation
between frequency of affixes and their productivity. Among the 50 most frequent and 50
most productive suffixes in Russian, 36 are common to both lists; among the 30 mostfrequent and 30 most productive prefixes, 28 are common (Kuznecova and Lavrenova,

65

Wolf Moskovich1975).
The idea of comparing word frequencies with their semantic properties is not new. There
were suggestions for creating a new type of semantic dictionary of the type of Roget's
Thesaurus with frequencies of individual words and semantic groups indicated. This idea
was put into practice in quantitative studies of poetic vocabulary (Abramova, 1974).
Further studies of this kind may provide a new insight into the structure of language.
Evaluating the state of the art of quantitative linguistics, one notices that althoughconsiderable efforts were made in the last 20 years to study the quantitative side of
linguistic events and valuable results were achieved, there remain more questions thananswers. Research in quantitative linguistics consumes too much time for mechanical work.
This work is like "a gigantic wall that a researcher built between himself and the text; it
serves as excuse of the fact that he did not chink of the text itself" (Guiraud, 1963, p. 45).
Even if the actual counting is not done manually but with the help of computers,
linguostatistical data received as a computer output are too voluminous to be evaluatedeasily. That is why special attention should be paid to the quality of the statistical
hypotheses suggested for testing. Statistical data reflect various tendencies and constraints
in language structure, often of a conflicting character, and it is not always easy (or evenpossible) to disentangle them. A negative attitude to'quantitative linguistics on the part ofsome linguists is a result of identifying the whole discipline with boring statisticalcomputations. The best side of quantitative linguistic research, the one bringing new
linguistic insights into the structure of language, remains unknown, or is considered
unimportant by the critics. Modern trends in theoretical linguistics are directed more
towards linguistic competence rather than performance.
Discussing conflicting approaches to language of theory-oriented linguists and dataoriented researchers in automated language processing, Montgomery writes:/ / one takes a negative point of view, these dichotomies represent irreconcilable
differences in the basic conception of language; more positively, they may be
regarded as complementary perspectives on the nature of language. The initial
issue is thus one of determining which view is correct. Should the positive view be
adopted, there is a more fundamental question as to the potential for unifying thetwo approaches to provide a balanced attack on problems of naluril language
analysis and description...
It is reasonable to consider the two approaches
complementary, since the specific weaknesses of the data-oriented position are
offset by corresponding strengths in theoretical orientation, and conversely (1969,
p. 12).
In our opinion, the inductive study of language structure, although it is now out of fashion,
will always be indispensable both for theoretical linguistics and for its applications.
Quantitative linguistics not only provides an explanation for a whole dimension of
language structure, but is of immediate use for various applications of linguistics.
Contribution of Quantitative Linguistics to Information Science (Retrospect and Prospect):
Conclusion
On the preceding pages, we returned several times to the assertion that although the aims ofresearch in quantitative linguistics and information science are different, these disciplines
treat the same material with the same or similar methods, and can only benefit by mutual
cooperation. After the revealing studies of Salton and his associates on the SMART project66

Perspective Paper: Quantitative Linguistics(Salton, 1971) and the appearance of the book by Sparck Jones and Kay (1973), no one candeny the fact that the application of vocabulary statistics to document retrieval brings more
immediate results than the use of other more sophisticated linguistic techniques. However,
although the pragmatic result is there, the work of statistical techniques has not received anadequate linguistic explanation within that or another linguistic theory, and it is not always
clear if statistical operations slide over the surface structure of text or touch deep structure
layers of language as well. There is no doubt that without touching the deep structure
layers of language, quantitative techniques could not produce meaningful results; but few
attempts were made to explain the inner mechanics of quantitative language analysis.
Providing the explanation of how and why statistical methods produce the results they do,
may be a major contribution of quantitative linguistics to information science. Such an
explanation may give a solid theoretical basis for the creation of new, more effective
information retrieval systems.
Quantitative linguistics contributes to information science at least in three domains:
1. It helps to create a lexicographic basis for information systems.
2. It helps to solve the problems of automaticcomparison of documents.

indexing, abstracting

and

3. It helps to elucidate complex quantitative laws of text organisation.
In recent years, there has been a noticeable tendency towards mutual understanding andcooperation between quantitative linguists and informatior scientists. A number of
information retrieval projects were devised b> quantitative linguists, and special
quantitative studies of texts and compilation of frequency dictionaries of words and word
combinations were conducted prior to actual building and testing of an information
retrieval system (e.g., the Laboratory of Applied Linguistics of the Moscow State University
devised a large information system for processing data on military field operations built
entirely on quantitative linguistic criteria (Kolgushkin, 1970); all the linguistic componentsof this system are based on previously compiled frequency dictionaries of words, wordpairs and n-tuples). Authors of frequency dictionaries often declare the aim of their work
to be information science applications.
Of particular importance both for linguistics and information science is research in thefield of distributive-statistical techniques of text analysis. Associative nets of words built
by distributive-statistical techniques have some peculiar characteristics which make them
particularly suitable for information retrieval purposes: they include only the terms that
actually appeared in documents; they are created by an algorithmic procedure which can be
repeated by the computer on texts of any length and any subject (that makes the procedure
extremely attractive for new, rapidly developing areas of science for which no dictionaries
or thesauri are available); they reflect specific statistical links of words which cannot be
detected by any other known linguistic method. In the course of testing and implementing
the distributive-statistical method of thesauri construction on large text samples, somemajor problems of automatic information retrieval are being solved as a by-product (e.g.,elementary statistical parameters, such as average frequency and dispersion, are used as
criteria for discriminating terms from nonterms; measures of cooccurrence of words lead tolists of set word combinations which are to be included as independent lexical units into
dictionaries of automatic information retrieval systems). Though much was done in thisarea, much more has to be done since a whole gamut of theoretical problems has not yet
been solved, such as criteria for delimitation of parts of an association net into semantic
fields; criteria for distinguishing substantial links of terms from unsubstantial ones; criteria67

W o l f Moskovichf o r distinguishing
performance f r o m

the effects of d i f f e r e n t associative term structures on retrieval
those of other i n f o r m a t i o n retrieval system components; etc.

One can foresee a wider application o f machine-built thesauri based on distributivestatistical text analysis in the years to come. Initial hopes thai the use of such thesauri
might lead to f u l l y automatic i n f o r m a t i o n retrieval systems seem to have given way tomore realistic expectations of a wider use of automaticthesauriin man-machinei n f o r m a t i o n retrieval systems of interactive searching. As more knowledge is gained on the
linguistic nature of these thesauri, of their properties and the influence of these properties
on retrieval performance, distributive-statistical thesauri w i l l be more and more subjected
to human postediting and correction w i i h some semantic links being removed and others,
more useful f o r retrieval purposes, given p r i o r i t y or introduced.
Distributive-statisticalthesauri may prove to be extremely useful f o r a r t i f i c i a l intelligence systems.
W i t h the advent o f user-oriented natural language i n f o r m a t i o n retrieval systems, atendency to incorporate statistical and distributive-statistical algorithms into more generalalgorithms f o r total automatic text analysis (including morphological, syntactic andsemantic analysis) may be expected. The role o f the linguostatistical component of such
systems w i l l be extremely important f o r the automatic f o r m a t i o n of thesauri, text
compression and weighting of keywords, sentences and larger parts of t e x t
Contrary to the opinion prevailing among research workers in automatic data processingthat precoordinate indexing systems w i l l become obsolete in the near future, a wide use o fsuch systems o f document classification as Universal Decimal Classification, International
Patent Classification and possibly other document classifications of new types is to be
expected. These events w i l l call f o r t h more attention to work in automatic classificationindexing based on linguostatistical criteria. There are various possibilities of organizingclassification work in terms o f man-machine interaction, e.g., w i t h the computer providing
a tentative list of classification tags for a document to be subsequently used by an indexer.
Quantitative linguistics has still to answer seemingly easy, but actually extremelycomplicated questions: What is a semantic unit f o r counting? What are the criteria f o r
d e l i m i t i n g words in running text? In what instances is a word combination to be considereda semantic unit equivalent to a word in counting?
In future uses of linguostatistical techniques f o r i n f o r m a t i o n retrieval, more attention w i l lbe paid to morphological analysis reduction of words to stems, conflation o f singular andplural forms o f the same word, etc.
Such simple devices considerably improve theperformance o f i n f o r m a t i o n retrieval systems.
Quantitative linguistics w i l l have its impact on future research on text units larger than asentence. Work on automatic abstracting is dependent upon progress in this area. It
remains to investigate which units may be chosen as representatives o f the contents o f adocument and by what criteria they may be selected.
In conclusion, it remains to add that justified criticism or reevaluation o f the contributionof linguistics to its various applications, be it the A L P A C report (1966) or the review bookby Sparck Jones and Kay, only furthers the progress of linguistics. It helps us to identify
our weak points, and to develop strategies f o r coping with the current situation in appliedfields. It would be wrong to draw exclusively negative, pessimistic conclusions f r o m theexistence o f this criticism. Most of the problems of automatic text processing, including
i n f o r m a t i o n retrieval, are of a linguistic nature, and w i l l be solved in the course o f time by68Perspective Paper: Quantitative Linguistics
proper, more advanced linguistic techniques. In future developments, the techniques ofquantitative linguistics will be interlocked more closely with other linguistic techniques andwithout any doubt will be extremely useful for various applied fields. The specificcharacter of quantitative linguistics as a linguistic discipline comes from the fact that it is
the properties of linguistic units, and not just their frequencies that stand at the focus of
its attention. The linguist begins quantitative linguistic research with an initial phase of
establishing a unit of counting and formulating a hypothesis. But his linguistic work
proper starts only when the numbers have been counted and he has to interpret them.
It is this author's contention therefore, that quantitative linguistics is indeed a major area
within linguistics, and is not, as some authorities contend, merely a branch of appliedmathematics beyond the boundaries of linguistics. Moreover, it is, we feel, an area which
has already contributed much, and Ihrough bold and innovative research, has much more to
add to the progress of information science.
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