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Original Articles

The frequency spectrum of text and vocabulary

Pages 38-50 | Published online: 21 Jul 2008

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Andrij Rovenchak & Solomija Buk. (2018) Part-of-Speech Sequences in Literary Text: Evidence From Ukrainian. Journal of Quantitative Linguistics 25:1, pages 1-21.
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David Mitchell. (2015) Type-token models: a comparative study. Journal of Quantitative Linguistics 22:1, pages 1-21.
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George Mikros, Nick Hatzigeorgiu & George Carayannis. (2005) Basic Quantitative Characteristics of the Modern Greek Language Using the Hellenic National Corpus. Journal of Quantitative Linguistics 12:2-3, pages 167-184.
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Articles from other publishers (22)

N. J. Enfield. (2023) Scale in Language. Cognitive Science 47:10.
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Álvaro Corral, Isabel Serra & Ramon Ferrer-i-Cancho. (2020) Distinct flavors of Zipf's law and its maximum likelihood fitting: Rank-size and size-distribution representations. Physical Review E 102:5.
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Bernardino Casas, Antoni Hernández-Fernández, Neus Català, Ramon Ferrer-i-Cancho & Jaume Baixeries. (2019) Polysemy and brevity versus frequency in language. Computer Speech & Language 58, pages 19-50.
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John G. Benjafield. (2019) Keyword frequencies in anglophone psychology. Scientometrics 118:3, pages 1051-1064.
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Nicolas Bérubé, Maxime Sainte-Marie, Philippe Mongeon & Vincent Larivière. (2018) Words by the tail: Assessing lexical diversity in scholarly titles using frequency-rank distribution tail fits. PLOS ONE 13:7, pages e0197775.
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Andrij Rovenchak. (2018) Telling apart Felidae and Ursidae from the distribution of nucleotides in mitochondrial DNA . Modern Physics Letters B 32:05, pages 1850057.
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SHOHEI HIDAKA. (2015) Estimating the latent number of types in growing corpora with reduced cost–accuracy trade-off. Journal of Child Language 43:1, pages 107-134.
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Andrij Rovenchak. (2015) Models of Frequency Spectrum in Texts Based on Quantum Distributions in Fractional Space Dimensions. Models of Frequency Spectrum in Texts Based on Quantum Distributions in Fractional Space Dimensions.
Weibing Deng, Armen E. Allahverdyan, Bo Li & Qiuping A. Wang. (2014) Rank-frequency relation for Chinese characters. The European Physical Journal B 87:2.
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Scott A. Crossley, Tom Cobb & Danielle S. McNamara. (2013) Comparing count-based and band-based indices of word frequency: Implications for active vocabulary research and pedagogical applications. System 41:4, pages 965-981.
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Gabriela Castañeda‐Jiménez & Scott Jarvis. 2013. The Handbook of Spanish Second Language Acquisition. The Handbook of Spanish Second Language Acquisition 498 513 .
Martin Gerlach & Eduardo G. Altmann. (2013) Stochastic Model for the Vocabulary Growth in Natural Languages. Physical Review X 3:2.
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J. A. Tenreiro Machado, Carla M. A. Pinto & A. Mendes Lopes. (2013) Power Law and Entropy Analysis of Catastrophic Phenomena. Mathematical Problems in Engineering 2013, pages 1-10.
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Scott Jarvis. 2012. Cross-Disciplinary Advances in Applied Natural Language Processing. Cross-Disciplinary Advances in Applied Natural Language Processing 50 72 .
Roderick Edwards & Laura Collins. (2011) Lexical Frequency Profiles and Zipf's Law. Language Learning 61:1, pages 1-30.
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Ioan-Iovitz Popescu, Gabriel Altmann & Reinhard Köhler. (2009) Zipf’s law—another view. Quality & Quantity 44:4, pages 713-731.
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Ramon Ferrer-i-Cancho & Ricard Gavaldà. (2009) The frequency spectrum of finite samples from the intermittent silence process. Journal of the American Society for Information Science and Technology 60:4, pages 837-843.
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Leo Egghe & Ronald Rousseau. (2006) Systems without low-productive sources. Information Processing & Management 42:6, pages 1428-1441.
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R. Ferrer i Cancho. (2005) Zipf's law from a communicative phase transition. The European Physical Journal B 47:3, pages 449-457.
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R. Ferrer i Cancho. (2005) The variation of Zipf?s law in human language. The European Physical Journal B 44:2, pages 249-257.
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RAMON FERRER i CANCHO & RICARD V. SOLÉ. (2011) ZIPF'S LAW AND RANDOM TEXTS. Advances in Complex Systems 05:01, pages 1-6.
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William Aiello, Fan Chung & Linyuan Lu. 2002. Handbook of Massive Data Sets. Handbook of Massive Data Sets 97 122 .

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