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While the use of statistical physics methods to analyze large corpora hasbeen useful to unveil many patterns in texts, no comprehensive investigationhas been performed investigating the properties of statistical measurementsacross different languages and texts. In this study we propose a framework thataims at determining if a text is compatible with a natural language and whichlanguages are closest to it, without any knowledge of the meaning of the words.The approach is based on three types of statistical measurements, i.e. obtainedfrom first-order statistics of word properties in a text, from the topology ofcomplex networks representing text, and from intermittency concepts where textis treated as a time series. Comparative experiments were performed with theNew Testament in 15 different languages and with distinct books in English andPortuguese in order to quantify the dependency of the different measurements onthe language and on the story being told in the book. The metrics found to beinformative in distinguishing real texts from their shuffled versions includeassortativity, degree and selectivity of words. As an illustration, we analyzean undeciphered medieval manuscript known as the Voynich Manuscript. We showthat it is mostly compatible with natural languages and incompatible withrandom texts. We also obtain candidates for key-words of the Voynich Manuscriptwhich could be helpful in the effort of deciphering it. Because we were able toidentify statistical measurements that are more dependent on the syntax than onthe semantics, the framework may also serve for text analysis inlanguage-dependent applications.

Probing the statistical properties of unknown texts: application to the Voynich Manuscript