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Towards The Quantification Of The Semantic Information Encoded In Written Language

Author

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  • MARCELO A. MONTEMURRO

    (Faculty of Life Sciences, The University of Manchester, M13 9PT, Manchester, United Kingdom)

  • DAMIÁN H. ZANETTE

    (Consejo Nacional de Investigaciones Científicas y Técnicas, Centro Atómico Bariloche and Instituto Balseiro, 8400 San Carlos de Bariloche, Río Negro, Argentina)

Abstract

Written language is a complex communication signal capable of conveying information encoded in the form of ordered sequences of words. Beyond the local order ruled by grammar, semantic and thematic structures affect long-range patterns in word usage. Here, we show that a direct application of information theory quantifies the relationship between the statistical distribution of words and the semantic content of the text. We show that there is a characteristic scale, roughly around a few thousand words, which establishes the typical size of the most informative segments in written language. Moreover, we find that the words whose contributions to the overall information is larger, are the ones more closely associated with the main subjects and topics of the text. This scenario can be explained by a model of word usage that assumes that words are distributed along the text in domains of a characteristic size where their frequency is higher than elsewhere. Our conclusions are based on the analysis of a large database of written language, diverse in subjects and styles, and thus are likely to be applicable to general language sequences encoding complex information.

Suggested Citation

  • Marcelo A. Montemurro & Damián H. Zanette, 2010. "Towards The Quantification Of The Semantic Information Encoded In Written Language," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 13(02), pages 135-153.
    • Handle: RePEc:wsi:acsxxx:v:13:y:2010:i:02:n:s0219525910002530
    DOI: 10.1142/S0219525910002530
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    Citations

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    Cited by:

    1. Marcelo A Montemurro & Damián H Zanette, 2013. "Keywords and Co-Occurrence Patterns in the Voynich Manuscript: An Information-Theoretic Analysis," PLOS ONE, Public Library of Science, vol. 8(6), pages 1-9, June.
    2. Shuo Xu & Ling Li & Xin An & Liyuan Hao & Guancan Yang, 2021. "An approach for detecting the commonality and specialty between scientific publications and patents," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(9), pages 7445-7475, September.
    3. Carretero-Campos, C. & Bernaola-Galván, P. & Coronado, A.V. & Carpena, P., 2013. "Improving statistical keyword detection in short texts: Entropic and clustering approaches," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(6), pages 1481-1492.
    4. Usó-Doménech, J.L. & Nescolarde-Selva, J.A. & Lloret-Climent, M. & Gash, H., 2016. "Semantics of language for ecosystems modelling: A model case," Ecological Modelling, Elsevier, vol. 328(C), pages 85-94.
    5. Cárdenas, Juan Pablo & González, Iván & Vidal, Gerardo & Fuentes, Miguel Angel, 2016. "Does network complexity help organize Babel’s library?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 447(C), pages 188-198.

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