Using complex networks to quantify consistency in the use of words

Abstract

In this paper we quantify the consistency of word usage in written texts represented by complex networks, where words were taken as nodes, by measuring the degree of preservation of the node neighborhood. Words were considered highly consistent if the authors used them with the same neighborhood. When ranked according to the consistency of use, the words obeyed a log-normal distribution, in contrast to the Zipfs law that applies to the frequency of use. Consistency correlated positively with the familiarity and frequency of use, and negatively with ambiguity and age of acquisition. An inspection of some highly consistent words confirmed that they are used in very limited semantic contexts. A comparison of consistency indices for 8 authors indicated that these indices may be employed for author recognition. Indeed, as expected authors of novels could be distinguished from those who wrote scientific texts. Our analysis demonstrated the suitability of the consistency indices, which can now be applied in other tasks, such as emotion recognition.

The multiplex structure of the mental lexicon influences picture naming in people with aphasia

“…Typically free associations are collected empirically from subjects who report the first word that comes to mind when presented with a given cue (e.g., for the cue 'dog', responses might include 'cat', 'pet' and 'ball'), and have been used for decades in memory and language studies (e.g., [43]). Furthermore, free associations have been used for network analyses of semantic memory [6] and to capture cognitive processes (e.g., [7][8][9]). We obtained free associations from the Edinburgh Associative Thesaurus [44], where only free associations provided by at least two different subjects were considered in order to filter out idiosyncratic associations [43,45].…”

“…Previous work on cognitive language networks has typically examined several types of 'single-layer networks' were only one type of edge is represented in the network at a time in order to more carefully consider different aspects of language. For example, semantic networks of word associations [5,6] have been used to identify writing styles [7], to quantify creativity [8] and for modelling word acquisition in young children [9]. On the other hand, phonological networks capturing phonological overlap between words [10] have been used to better understand word acquisition [11,12] and spoken word recognition and production [13].…”

Probing the Statistical Properties of Unknown Texts: Application to the Voynich Manuscript

“…The selectivity is related to the effect of word consistency [29] (see Ref. [21]) which was verified to be common in English, especially for very frequent words. The number of bigrams B is also informative, which means that in natural languages it is unlikely that the same word is repeated (when compared with random texts).…”

An analytic solution of a model of language competition with bilingualism and interlinguistic similarity

“…The science of complex systems should be highlighted because of its very clever usage of existing mathe-matical methods that stem mainly from statistical mechanics [4]. While also appraising other important contributions that widen our knowledge about the nature of human languages [6,5,7,8,9,10,11,12], we shall focus on a seminal paper by Abrams and Strogatz [13] that prominently triggered research in its direction. For an exhaustive review on very varied related topics with up-to-date bibliography consult [14]; and for a more extended review on the impact of statistical physics on social dynamics, including language modeling, see [15].…”