## 1 Introduction and Motivations
The underlying idea is that simple mechanisms, inspired by basic human linguistic capabilities [16], can lead to an emergent collective behavior, representing an implicit structure of the sentence in terms of relationships between words. With this approach, the most important consideration in the modeling is that global (_i.e._, language) -level relationships between words must not be explicitly modeled, neither in logical nor mathematical terms. Such relationships must be kept _embodied_ in the corpus [9]. Indeed, in contrast with a cognitivist system, which does not need to be embodied, an emergent system is dependent on the physical platform in which it is implemented, _i.e._, the platform in which the corpus itself resides [9]. When using the Web as a corpus, representativeness and correctness are two important topics of debate [1,18,19]. With regard to representativeness, let us consider some typical events of human conversation and their availability in both web-based and conventional text. _Production and reception_: many conversations have one speaker and one hearer; this one-to-one conversation is largely available on the Web; in contrast, many conventional text have one writer and many readers, e.g., a Times newspaper article. _Speech and text_: there are orders of magnitude more speech events than writing events; web-based messaging is very close to speech events; in contrast, most conventional corpus research has tended to focus on text production rather than on speech production. _Background language_: rumors and murmurs are conversational events greatly available on social networks; in contrast, these kinds of events are poorly covered by conventional text. _Copying_: in the text domain, copyright, ownership and plagiarism restrict cut-and-paste authorship, whereas in the Web domain the open access paradigm enables new language production events. With regard to correctness, a fundamental paradigm shift has been occurring since the introduction of the Web as a corpus. In contrast to paper-based, copy-edited published texts, web-based texts are produced by a large variety of authors, cheaply and rapidly with little concerns for formal correctness [1]. For instance, a Google search for \"I beleave\", \"I beleive\", and \"I believe\" gives 257,000, 3,440,000, and 278,000,000 hits, respectively. Hence, all the \"erroneous\" forms appear, but much less often than the \"correct\" forms. From the formal standpoint, the Web is a dirty corpus, but expected usage is much more frequent than what might be considered noise. Actually, a language is made of a core of lexis, grammar, constructions, plus a wide array of sublanguages, used in each of a myriad of human activities. In the last decade, an extensive literature on sophisticated mathematical model for word frequency distributions has been produced with the aim of modeling sublanguage mixtures [1,20]. Let us consider a simple positive feedback: for a given sentence, the more occurrences of the sentence in a corpus, the more correctness of the sentence [21]. Here, the _open-world assumption_ is considered: any phraseology that is used in some sublanguage events of human conversation can be positively assessed [20]. However, it is unlikely that many occurrences of the same sentence are found in a corpus [1]. Moreover, for an incorrect sentence it should be important to show which part of the sentence is actually incorrect. Hence, a structural analysis of the sentence able to allow emerging relationships between words should be considered. Here, we emphasize that this analysis should be performed at the syntagmatic level, by identifying and rating elementary segments within the text (syntagms) [22]. Nevertheless, the number of occurrences of a text segment is strongly affected by the usage of its terms. For instance, unfamiliar proper nouns and unusual numbers may drastically limit the number of occurrences of a segment.