In narrative texts punctuation marks obey the same statistics as words

From a grammar point of view, the role of punctuation marks in a sentence is formally defined and well understood. In semantic analysis punctuation plays also a crucial role as a method of avoiding ambiguity of the meaning. A different situation can be observed in the statistical analyses of language samples, where the decision on whether the punctuation marks should be considered or should be neglected is seen rather as arbitrary and at present it belongs to a researcher’s preference. An objective of this work is to shed some light onto this problem by providing us with an answer to the question whether the punctuation marks may be treated as ordinary words and whether they should be included in any analysis of the word cooccurences. We already know from our previous study (S. Drożdż et al., Inf. Sci. 331 (2016) 32-44) that full stops that determine the length of sentences are the main carrier of long-range correlations. Now we extend that study and analyze statistical properties of the most common punctuation marks in a few Indo-European languages, investigate their frequencies, and locate them accordingly in the Zipf rank-frequency plots as well as study their role in the word-adjacency networks. We show that, from a statistical viewpoint, the punctuation marks reveal properties that are qualitatively similar to the properties of the most frequent words like articles, conjunctions, pronouns, and prepositions. This refers to both the Zipfian analysis and the network analysis. By adding the punctuation marks to the Zipf plots, we also show ∗Corresponding author: [email protected] Preprint submitted to Information Sciences September 29, 2016 ar X iv :1 60 4. 00 83 4v 2 [ cs .C L ] 2 7 Se p 20 16 that these plots that are normally described by the Zipf-Mandelbrot distribution largely restore the power-law Zipfian behaviour for the most frequent items. Our results indicate that the punctuation marks can fruitfully be considered in the linguistic studies as their inclusion effectively extends dimensionality of an analysis and, therefore, it opens more space for possible manifestation of some previously unobserved effects.