Scaling laws and model of words organization in spoken and written language

Abstract

A broad range of complex physical and biological systems exhibits scaling laws. The human language is a complex system of words organization. Studies of written texts have revealed intriguing scaling laws that characterize the frequency of words occurrence, rank of words, and growth in the number of distinct words with text length. While studies have predominantly focused on the language system in its written form, such as books, little attention is given to the structure of spoken language. Here we investigate a database of spoken language transcripts and written texts, and we uncover that words organization in both spoken language and written texts exhibits scaling laws, although with different crossover regimes and scaling exponents. We propose a model that provides insight into words organization in spoken language and written texts, and successfully accounts for all scaling laws empirically observed in both language forms.

Model of the Dynamic Construction Process of Texts and Scaling Laws of Words Organization in Language Systems

“…Note that, in Fig 5 we also include the results for English spoken language we analyzed in a previous study [30]. The data points obtained from English spoken language fall nicely on the fitting curves, which further validate the functional relation we establish in this study between the empirically observed scaling exponents and the model parameters.…”

“…Data points are obtained from the scaling analyses and simulation of all ten Chinese and English language books listed in Table 1, and English spoken language from Ref. [30]. The dotted lines indicate 95% confidence intervals of the data points obtained from empirical and model parameters for each separate book.…”

Evaluating the Irregularity of Natural Languages

“…Next, languages were labeled in classes according to the linguistic family to which they belong (Romance, Germanic, Slavic, Uralic). The eight dimensional vectors comprising the eight ApEn (pattern lengths [3][4][5][6][7][8][9][10] values are used to create the two-dimensional projection. We observe that the families are segregated.…”

“…Two representative findings of universal features of natural language are the Zipf and Heaps laws, which are based on word frequency and number of different words, respectively [1][2][3][4]. From a more basic perspective, human language can also be considered as a sequence of symbols which contains information encoded in the patterns (words) needed to communicate.…”

Indian English Evolution and Focusing Visible Through Power Laws

“…Rank frequency distributions are found in contemporary natural language corpora and Swadesh lists [19][20][21], comparisons across multiple languages [22][23][24][25], in both written and spoken language data [26], across all English literary texts included in Project Gutenberg [27], and historic language data that is not yet translated [28], but, importantly, are not found in random monkey-typing corpora [14,29]. Rank frequency research has expanded beyond a narrow focus on adult, monolingual, native speakers to demonstrate distinct rank frequency distributions for corpora of varying levels of L2 proficiency across users of natural language [30,31] and artificial command languages [32], L1 attritors who have lost proficiency in their L1 over their lifespan [31], different language combinations of spontaneous codeswitching [33], and in languages with varying proportions of non-native speakers [34].…”