A Theory of Word-Frequency Distribution

Parker-Rhodes, A. F.; Joyce, Terry

doi:10.1038/1781308a0

Cited by 15 publications

(10 citation statements)

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“…9) sketched a simple vocabulary growth model. Parker-Rhodes and Joyce (1956) argued that the distribution arises by a linear search through words in long-term memory ordered by frequency during normal language processing, where the time required to scan a word is proportional to the number of words scanned. To date, there is no evidence for this kind of process in normal language use.…”

Section: Models Of Zipf’s Lawmentioning

confidence: 99%

“…To give a brief picture of the range of explanations that have been worked out, such distributions have been argued to arise from random concatenative processes (Conrad & Mitzenmacher, 2004; Li, 1992; Miller, 1957), mixtures of exponential distributions (Farmer & Geanakoplos, 2006), scale-invariance (Chater & Brown, 1999), (bounded) optimization of entropy (Mandelbrot, 1953) or Fisher information (Hernando, Puigdomènech, Villuendas, Vesperinas & Plastino, 2009), the invariance of such power laws under aggregation (see Farmer & Geanakoplos, 2006), multiplicative stochastic processes (see Mitzenmacher, 2004), preferential reuse (Simon, 1955; Yule, 1944), symbolic descriptions of complex stochastic systems (Corominas-Murtra & Solé, 2010), random walks on logarithmic scales (Kawamura & Hatano, 2002), semantic organization (Guiraud, 1968; D. Manin, 2008), communicative optimization (Ferrer i Cancho, 2005a, b; Ferrer i Cancho & Solé, 2003; Mandelbrot, 1962; Salge, Ay, Polani, & Prokopenko, 2013; Zipf, 1936, 1949), random division of elements into groups (Baek, Bernhardsson & Minnhagen 2011), first- and second-order approximation of most common (e.g., normal) distributions (Belevitch, 1959), and optimized memory search (Parker-Rhodes & Joyce, 1956), among many others.…”

Section: Introductionmentioning

confidence: 99%

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Zipf’s word frequency law in natural language: A critical review and future directions

2014

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The frequency distribution of words has been a key object of study in statistical linguistics for the past 70 years. This distribution approximately follows a simple mathematical form known as Zipf ’ s law. This article first shows that human language has a highly complex, reliable structure in the frequency distribution over and above this classic law, although prior data visualization methods have obscured this fact. A number of empirical phenomena related to word frequencies are then reviewed. These facts are chosen to be informative about the mechanisms giving rise to Zipf’s law and are then used to evaluate many of the theoretical explanations of Zipf’s law in language. No prior account straightforwardly explains all the basic facts or is supported with independent evaluation of its underlying assumptions. To make progress at understanding why language obeys Zipf’s law, studies must seek evidence beyond the law itself, testing assumptions and evaluating novel predictions with new, independent data.

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Section: Models Of Zipf’s Lawmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Zipf’s word frequency law in natural language: A critical review and future directions

2014

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“…The rank-frequency of words in natural language follows a family of power law distributions; such distributions are known as Zipf's law (Zipf, 1936(Zipf, , 1949). Zipf's law has been receiving tremendous attentions in statistical/quantitative linguistics for more than 70 years (Zipf, 1936(Zipf, , 1949Parker-Rhodes and Joyce, 1956;Simon, 1955Simon, , 1960Mandelbrot, 1953Mandelbrot, , 1961Carroll, 1967;Chen, 1991;Li, 1992Li, , 2002Corominas-Murtra and Solé, 2010;Piantadosi, 2014). While these research narrow the power law distributions in the words' rank-frequency, we discover that the power law distributions also appear in another form of natural language: entity length.…”

Section: Introductionmentioning

confidence: 99%

Discovering power laws in computer programs

Zhang

2009

Information Processing & Management

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“…After completing the original qualitative data coding, to answer the second research question, we used the frequency statistics method to analyze the frequency of support needs of college student entrepreneurs ( Parker-Rhodes and Joyce, 1956 ). The frequency analysis method is commonly used to evaluate the importance of research objects.…”

Section: Methodsmentioning

confidence: 99%

“…To investigate the students' ranking of the importance of the support needs during the different entrepreneurial stages, we conducted further frequency analysis on the distribution (Parker-Rhodes and Joyce, 1956). The results are shown in Table 4.…”

Section: Distribution Of Support Needs Across the Entrepreneurial Stagesmentioning

confidence: 99%

Give Me What I Want: Identifying the Support Needs of College Student Entrepreneurs

Wang

Huang

2020

Front. Psychol.

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To determine how the success rate of college students' entrepreneurship can be improved, we sought to identify the support needs of undergraduate entrepreneurs and evaluate the importance of different support needs using data from face-to-face interviews with 138 Chinese college student entrepreneurs. Further, we explored the distribution tendencies of the support needs at different entrepreneurial stages. We found that the support needs of college student entrepreneurs can be divided into 20 types, which can be categorized into core support needs (six types), secondary support needs (six types), and marginal support needs (eight types) in descending order of importance. A cross-stage analysis indicates that the support needs differ during the different entrepreneurial stages. While the needs during the entrepreneurial preparation stage and the startup stage have many similarities, those during the entrepreneurial failure stage are significantly different from those of the other three stages examined, showing heterogeneity. In addition, we identified psychological counseling as an important core support need in the failure stage and demonstrate its critical role in helping college students out of the shadow of entrepreneurial failure. This study expands the research on entrepreneurial support needs. Its findings provide valuable information to help colleges and governments develop more targeted and appropriate support for college entrepreneurs based on entrepreneurial stages to promote entrepreneurial success.

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A Theory of Word-Frequency Distribution

Cited by 15 publications

References 1 publication

Zipf’s word frequency law in natural language: A critical review and future directions

Zipf’s word frequency law in natural language: A critical review and future directions

Discovering power laws in computer programs

Give Me What I Want: Identifying the Support Needs of College Student Entrepreneurs

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