Zipf's law
Probability mass function
Zipf PMF for N = 10 on a log–log scale. The horizontal axis is the index k . (Note that the function is only defined at integer values of k. The connecting lines do not indicate continuity.) 

Cumulative distribution function
Zipf CDF for N = 10. The horizontal axis is the index k . (Note that the function is only defined at integer values of k. The connecting lines do not indicate continuity.) 

Parameters  (real) (integer) 

Support  
pmf  
CDF  
Mean  
Mode  
Entropy  
MGF  
CF 
Zipf's law /ˈzɪf/, an empirical law formulated using mathematical statistics, refers to the fact that many types of data studied in the physical and social sciences can be approximated with a Zipfian distribution, one of a family of related discrete power law probability distributions. The law is named after the American linguist George Kingsley Zipf (1902–1950), who popularized it and sought to explain it (Zipf 1935, 1949), though he did not claim to have originated it.^{[1]} The French stenographer JeanBaptiste Estoup (1868–1950) appears to have noticed the regularity before Zipf.^{[2]} It was also noted in 1913 by German physicist Felix Auerbach^{[3]} (1856–1933).
Contents
Motivation
Zipf's law states that given some corpus of natural language utterances, the frequency of any word is inversely proportional to its rank in the frequency table. Thus the most frequent word will occur approximately twice as often as the second most frequent word, three times as often as the third most frequent word, etc.: the rankfrequency distribution is an inverse relation. For example, in the Brown Corpus of American English text, the word "the" is the most frequently occurring word, and by itself accounts for nearly 7% of all word occurrences (69,971 out of slightly over 1 million). True to Zipf's Law, the secondplace word "of" accounts for slightly over 3.5% of words (36,411 occurrences), followed by "and" (28,852). Only 135 vocabulary items are needed to account for half the Brown Corpus.^{[4]}
The same relationship occurs in many other rankings unrelated to language, such as the population ranks of cities in various countries, corporation sizes, income rankings, ranks of number of people watching the same TV channel,^{[5]} and so on. The appearance of the distribution in rankings of cities by population was first noticed by Felix Auerbach in 1913.^{[3]} Empirically, a data set can be tested to see whether Zipf's law applies by checking the goodness of fit of an empirical distribution to the hypothesized power law distribution with a KolmogorovSmirnov test, and then comparing the (log) likelihood ratio of the power law distribution to alternative distributions like an exponential distribution or lognormal distribution.^{[6]} When Zipf's law is checked for cities, a better fit has been found with b = 1.07; i.e. the largest settlement is the size of the largest settlement. While Zipf's law holds for the upper tail of the distribution, the entire distribution of cities is lognormal and follows Gibrat's law.^{[7]} Both laws are consistent because a lognormal tail can typically not be distinguished from a Pareto (Zipf) tail.
Theoretical review
Zipf's law is most easily observed by plotting the data on a loglog graph, with the axes being log (rank order) and log (frequency). For example, the word "the" (as described above) would appear at x = log(1), y = log(69971). It is also possible to plot reciprocal rank against frequency or reciprocal frequency or interword interval against rank.^{[1]} The data conform to Zipf's law to the extent that the plot is linear.
Formally, let:
 N be the number of elements;
 k be their rank;
 s be the value of the exponent characterizing the distribution.
Zipf's law then predicts that out of a population of N elements, the frequency of elements of rank k, f(k;s,N), is:
Zipf's law holds if the number of occurrences of each element are independent and identically distributed random variables with power law distribution ^{[8]}
In the example of the frequency of words in the English language, N is the number of words in the English language and, if we use the classic version of Zipf's law, the exponent s is 1. f(k; s,N) will then be the fraction of the time the kth most common word occurs.
The law may also be written:
where H_{N,s} is the Nth generalized harmonic number.
The simplest case of Zipf's law is a "^{1}⁄_{f} function". Given a set of Zipfian distributed frequencies, sorted from most common to least common, the second most common frequency will occur ½ as often as the first. The third most common frequency will occur ⅓ as often as the first. The n^{th} most common frequency will occur ^{1}⁄_{n} as often as the first. However, this cannot hold exactly, because items must occur an integer number of times; there cannot be 2.5 occurrences of a word. Nevertheless, over fairly wide ranges, and to a fairly good approximation, many natural phenomena obey Zipf's law.
Mathematically, the sum of all relative frequencies in a Zipf distribution is equal to the harmonic series, and
In human languages, word frequencies have a very heavytailed distribution, and can therefore be modeled reasonably well by a Zipf distribution with an s close to 1.
As long as the exponent s exceeds 1, it is possible for such a law to hold with infinitely many words, since if s > 1 then
where ζ is Riemann's zeta function.
Statistical explanation
Although Zipf’s Law holds for most languages, even for nonnatural languages like Esperanto,^{[9]} the reason is still not well understood .^{[10]} However, it may be partially explained by the statistical analysis of randomly generated texts. Wentian Li has shown that in a document in which each character has been chosen randomly from a uniform distribution of all letters (plus a space character), the "words" follow the general trend of Zipf's law (appearing approximately linear on loglog plot).^{[11]} Vitold Belevitch in a paper, On the Statistical Laws of Linguistic Distribution offered a mathematical derivation. He took a large class of wellbehaved statistical distributions (not only the normal distribution) and expressed them in terms of rank. He then expanded each expression into a Taylor series. In every case Belevitch obtained the remarkable result that a firstorder truncation of the series resulted in Zipf's law. Further, a secondorder truncation of the Taylor series resulted in Mandelbrot's law.^{[12]}^{[13]}
The principle of least effort is another possible explanation: Zipf himself proposed that neither speakers nor hearers using a given language want to work any harder than necessary to reach understanding, and the process that results in approximately equal distribution of effort leads to the observed Zipf distribution.^{[14]}^{[15]}
Related laws
Zipf's law in fact refers more generally to frequency distributions of "rank data," in which the relative frequency of the nthranked item is given by the Zeta distribution, 1/(n^{s}ζ(s)), where the parameter s > 1 indexes the members of this family of probability distributions. Indeed, Zipf's law is sometimes synonymous with "zeta distribution," since probability distributions are sometimes called "laws". This distribution is sometimes called the Zipfian or Yule distribution.
A generalization of Zipf's law is the Zipf–Mandelbrot law, proposed by Benoît Mandelbrot, whose frequencies are:
The "constant" is the reciprocal of the Hurwitz zeta function evaluated at s. In practice, as easily observable in distribution plots for large corpora, the observed distribution can better be modelled as a sum of separate distributions for different subsets or subtypes of words that follow different parameterizations of the ZipfMandelbrot distribution, in particular the closed class of functional words exhibit "s" lower than 1, while openended vocabulary growth with document size and corpus size require "s" greater than 1 for convergence of the Generalized Harmonic Series.^{[1]}
Zipfian distributions can be obtained from Pareto distributions by an exchange of variables.^{[16]}
The Zipf distribution is sometimes called the discrete Pareto distribution^{[17]} because it is analogous to the continuous Pareto distribution in the same way that the discrete uniform distribution is analogous to the continuous uniform distribution.
The tail frequencies of the Yule–Simon distribution are approximately
for any choice of ρ > 0.
In the parabolic fractal distribution, the logarithm of the frequency is a quadratic polynomial of the logarithm of the rank. This can markedly improve the fit over a simple powerlaw relationship.^{[16]} Like fractal dimension, it is possible to calculate Zipf dimension, which is a useful parameter in the analysis of texts.^{[18]}
It has been argued that Benford's law is a special bounded case of Zipf's law,^{[16]} with the connection between these two laws being explained by their both originating from scale invariant functional relations from statistical physics and critical phenomena.^{[19]} The ratios of probabilities in Benford's law are not constant. The leading digits of data satisfying Zipf's law with s = 1 satisfies Benford's law.
Benford's law: 


1  0.30103000  
2  0.17609126  0.7735840 
3  0.12493874  0.8463832 
4  0.09691001  0.8830605 
5  0.07918125  0.9054412 
6  0.06694679  0.9205788 
7  0.05799195  0.9315169 
8  0.05115252  0.9397966 
9  0.04575749  0.9462848 
See also
References
 ↑ ^{1.0} ^{1.1} ^{1.2} Powers, David M W (1998). "Applications and explanations of Zipf's law". Association for Computational Linguistics: 151–160. Cite journal requires
journal=
(help); External link intitle=
(help)<templatestyles src="Module:Citation/CS1/styles.css"></templatestyles>  ↑ Christopher D. Manning, Hinrich Schütze Foundations of Statistical Natural Language Processing, MIT Press (1999), ISBN 9780262133609, p. 24
 ↑ ^{3.0} ^{3.1} Auerbach F. (1913) Das Gesetz der Bevölkerungskonzentration. Petermann’s Geographische Mitteilungen 59, 74–76
 ↑ Fagan, Stephen; Gençay, Ramazan (2010), "An introduction to textual econometrics", in Ullah, Aman; Giles, David E. A. (eds.), Handbook of Empirical Economics and Finance, CRC Press, pp. 133–153, ISBN 9781420070361<templatestyles src="Module:Citation/CS1/styles.css"></templatestyles>. P. 139: "For example, in the Brown Corpus, consisting of over one million words, half of the word volume consists of repeated uses of only 135 words."
 ↑ M. Eriksson, S.M. Hasibur Rahman, F. Fraille, M. Sjöström, ”Efficient Interactive Multicast over DVBT2  Utilizing Dynamic SFNs and PARPS”, 2013 IEEE International Conference on Computer and Information Technology (BMSB’13), London, UK, June 2013. Suggests a heterogeneous Zipflaw TV channelselection model
 ↑ Clauset, A., Shalizi, C. R., & Newman, M. E. J. (2009). PowerLaw Distributions in Empirical Data. SIAM Review, 51(4), 661–703. doi:10.1137/070710111
 ↑ Eeckhout J. (2004), Gibrat's law for (All) Cities. American Economic Review 94(5), 14291451.
 ↑ Adamic, Lada A."Zipf, Powerlaws, and Pareto  a ranking tutorial"
 ↑ Bill Manaris, Luca Pellicoro, George Pothering, Harland Hodges (13 February 2006). INVESTIGATING ESPERANTO’S STATISTICAL PROPORTIONS RELATIVE TO OTHER LANGUAGES USING NEURAL NETWORKS AND ZIPF’S LAW (PDF). Artificial Intelligence and Applications. Innsbruck, Austria. pp. 102–108.CS1 maint: multiple names: authors list (link)<templatestyles src="Module:Citation/CS1/styles.css"></templatestyles>
 ↑ Léon Brillouin, La science et la théorie de l'information, 1959, réédité en 1988, traduction anglaise rééditée en 2004
 ↑ Wentian Li (1992). "Random Texts Exhibit Zipf'sLawLike Word Frequency Distribution" (PDF). IEEE Transactions on Information Theory. 38 (6): 1842–1845. doi:10.1109/18.165464.<templatestyles src="Module:Citation/CS1/styles.css"></templatestyles>
 ↑ Neumann, Peter G. "Statistical metalinguistics and Zipf/Pareto/Mandelbrot", SRI International Computer Science Laboratory, accessed and archived 29 May 2011.
 ↑ Belevitch V (18 December 1959). "On the statistical laws of linguistic distributions". Annales de la Société Scientifique de Bruxelles. I. 73: 310–326.<templatestyles src="Module:Citation/CS1/styles.css"></templatestyles>
 ↑ Zipf GK (1949). Human Behavior and the Principle of Least Effort. Cambridge, Massachusetts: AddisonWesley. p. 1.<templatestyles src="Module:Citation/CS1/styles.css"></templatestyles>
 ↑ Ramon Ferrer i Cancho and Ricard V. Sole (2003). "Least effort and the origins of scaling in human language". Proceedings of the National Academy of Sciences of the United States of America. 100 (3): 788–791. doi:10.1073/pnas.0335980100. PMC 298679. PMID 12540826.<templatestyles src="Module:Citation/CS1/styles.css"></templatestyles>
 ↑ ^{16.0} ^{16.1} ^{16.2} Johan Gerard van der Galien (20031108). "Factorial randomness: the Laws of Benford and Zipf with respect to the first digit distribution of the factor sequence from the natural numbers".<templatestyles src="Module:Citation/CS1/styles.css"></templatestyles>
 ↑ N. L. Johnson, S. Kotz, and A. W. Kemp (1992). Univariate Discrete Distributions (second ed.). New York: John Wiley & Sons, Inc. ISBN 0471548979.CS1 maint: multiple names: authors list (link) CS1 maint: ref=harv (link)<templatestyles src="Module:Citation/CS1/styles.css"></templatestyles>, p. 466.
 ↑ Ali Eftekhari (2006) Fractal geometry of texts. Journal of Quantitative Linguistic 13(23): 177 – 193.
 ↑ L. Pietronero, E. Tosatti, V. Tosatti, A. Vespignani (2001) Explaining the uneven distribution of numbers in nature: The laws of Benford and Zipf. Physica A 293: 297 – 304.
Further reading
Primary:
 George K. Zipf (1949) Human Behavior and the Principle of Least Effort. AddisonWesley.
 George K. Zipf (1935) The Psychobiology of Language. HoughtonMifflin. (see citations at http://citeseer.ist.psu.edu/context/64879/0 )
Secondary:
 Lada Adamic. Zipf, Powerlaws, and Pareto  a ranking tutorial. http://www.hpl.hp.com/research/idl/papers/ranking/ranking.html
 Alexander Gelbukh and Grigori Sidorov (2001) "Zipf and Heaps Laws’ Coefficients Depend on Language". Proc. CICLing2001, Conference on Intelligent Text Processing and Computational Linguistics, February 18–24, 2001, Mexico City. Lecture Notes in Computer Science N 2004, ISSN 03029743, ISBN 3540416870, SpringerVerlag: 332–335.
 Damián H. Zanette (2006) "Zipf's law and the creation of musical context," Musicae Scientiae 10: 318.
 Kali R. (2003) "The city as a giant component: a random graph approach to Zipf's law," Applied Economics Letters 10: 717720(4)
 Gabaix, Xavier (August 1999). "Zipf's Law for Cities: An Explanation" (PDF). Quarterly Journal of Economics. 114 (3): 739–67. doi:10.1162/003355399556133. ISSN 00335533.<templatestyles src="Module:Citation/CS1/styles.css"></templatestyles>
 Axtell, Robert L; Zipf distribution of US firm sizes, Science, 293, 5536, 1818, 2001, American Association for the Advancement of Science
 Ramu Chenna, Toby Gibson; Evaluation of the Suitability of a Zipfian Gap Model for Pairwise Sequence Alignment,
International Conference on Bioinformatics Computational Biology: 2011.
External links
Wikimedia Commons has media related to [[commons:Script error: The function "getCommonsLink" does not exist.Script error: The function "getCommonsLink" does not exist.]]. 
 Strogatz, Steven (20090529). "Guest Column: Math and the City". The New York Times. Retrieved 20090529.<templatestyles src="Module:Citation/CS1/styles.css"></templatestyles>—An article on Zipf's law applied to city populations
 Seeing Around Corners (Artificial societies turn up Zipf's law)
 PlanetMath article on Zipf's law
 Distributions de type "fractal parabolique" dans la Nature (French, with English summary)
 An analysis of income distribution
 Zipf List of French words
 Zipf list for English, French, Spanish, Italian, Swedish, Icelandic, Latin, Portuguese and Finnish from Gutenberg Project and online calculator to rank words in texts
 Citations and the Zipf–Mandelbrot's law
 Zipf's Law for U.S. Cities by Fiona Maclachlan, Wolfram Demonstrations Project.
 Weisstein, Eric W., "Zipf's Law", MathWorld.
 Zipf's Law examples and modelling (1985)
 Complex systems: Unzipping Zipf's law (2011)
 Benford’s law, Zipf’s law, and the Pareto distribution by Terence Tao.
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 Discrete distributions
 Computational linguistics
 Power laws
 Statistical laws
 Empirical laws
 Tails of probability distributions
 Quantitative linguistics
 Bibliometrics
 Corpus linguistics