Search

Our work aims to evaluate the strength of the association between function words and several text types: novels, poems, academic articles, reviews, and blog posts, and the accuracy of their classification to these categories, through machine-learning and statistical methods. The principal conclusion is that the types of texts are distinguishable based only on the function words, either by vocabulary or vocabulary diversity. Such findings may impact the techniques of authorship attribution based on function words and text clustering techniques since some function words add information about the text types/genres, in addition to content words. [ABSTRACT FROM AUTHOR]

This article highlights the usual overlook in the literature of regular occurrences of low-frequency words (hapax legomena) in specific authors' texts. This overlook arises from a linguistic assumption of non-systematic and context-dependent low-frequency word occurrences in extensive texts, and from the tendency of SVM methods to mark low-frequency words as irrelevant compared to the more frequent lexicon (e.g. Boukhaled, M. A. and Ganascia, J.-G. (2015). Using function words for authorship attribution: bag-of-words vs. sequential rules. In The 11th International Workshop on Natural Language Processing and Cognitive Science, October 2014, Venice, Italy. de Gruyter, Natural Language Processing and Cognitive Science Proceedings 2014, pp. 115–122.). Many approaches to authorship attribution are based on the n most frequent 'function words', which (1) are grammatically essential, frequent, and therefore included in each text; (2) are not affected by the topic of the text; and (3) reflect the unintentional linguistic activity of the author (Binongo, J. N. G. (2003). Who wrote the 15th book of Oz? An application of multivariate analysis to authorship attribution. Chance, 16(2): 9–17). Hapax legomena meet these conditions as well, except frequency (Baayen, H. van Halteren, H. and Tweedie, F. (1996). Outside the cave of shadows: using syntactic annotation to enhance authorship attribution. Literary and Linguistic Computing, 11(3): 121–32). We test the hypothesis that hapax legomena may work for purposes of authorship attribution based on selecting only hapaxes from whole texts (or randomly selected tokens of hapaxes) while using a specific pre-processed input (eigendecomposition of a cosine distance matrix) to the SVM classifier. This method evaluated the attribution of texts from fourteen Czech authors (yielding ninety-one pairs in total) and Evert, S. Proisl, T. Jannidis, F. et al. (2017). Understanding and explaining Delta measures for authorship attribution. Digital Scholarship in the Humanities, 32(2): 4–16 data set, and proved itself a suitable tool for identifying authors of previously unknown texts. Our method identifies a sparse network of regular occurrences of low-frequency words in different authors' texts. [ABSTRACT FROM AUTHOR]

In this paper we show some interpretation of the genetic code design. We proceed from the discovery of DNA structure to current stage of the molecular biology. Generally we introduce the basic semiotic assumptions of molecular biology in the description of the structure of DNA, proteins and genetic code. We focus on interpretations of Francis Crick, another molecular biologists, biosemioticians and linguists. For the aims of the paper we describe some fundaments of molecular biology. Core of our text is quantitative analysis (n-gram structure, Zipf's law) of mRNA strings and natural language text. We take into consideration representative quantitative analysis of DNA, RNA and proteins too. Our analysis of mRNA confirms the assumption that the design of the genetic code cannot analogize DNA bases and letters. [ABSTRACT FROM AUTHOR]