## 1 Introduction and Motivations  
Human communication processes are nowadays increasingly integrated with the Web. As a result, a huge quantity of natural language text can be instantly accessed through search engines, as a live linguistic corpus [1]. This consists of a variety of text types and styles, such as colloquial, formal, technical, scientific, medical, legal, journalistic, and so on. With respect to edited texts, web-based texts are produced in a wider variety of contexts, with different writing styles. The problem of text checking in different contexts remains to a large extent still unsolved [2]. Human language is also influenced by evolutionary processes characterized by emergence, self-organization, collective behavior, clustering, diversification, hierarchy formation, and so on [3; 4; 5; 6]. For this reason, new research methodologies and trends based on direct observable data have gained an increasing interest in natural language processing (NLP) [7; 8]. In this paper, we present a text analysis approach that is intrinsically embodied in the Web and is based on the paradigm of _emergence_, in contrast with the classical and common paradigm of _cognitivism_[9]. In the following, we first classify NLP methods according to such paradigms, and then we provide a better characterization of our approach. Generally speaking, in the literature there are three basic approaches to NLP, _i.e._, symbolic, statistical and connectionist [10]. _Symbolic approaches_ are based on representation of knowledge about language, derived from human introspective data. Two examples of this category are the following: (i) Rule-Based Systems, which rely on morphological/syntactic generators [11]; (ii) Semantic Networks, which are based on a structure of labeled relations and concepts [12]. Quite different are _statistical approaches_, which employ directly observable data to develop a mathematical model of linguistic phenomena [13; 14]. Two examples of this category are the following: (i) Markov Models, which can predict the next symbol or word in a sequence [15; 16]; (ii) Language Usage Patterns, in which NLP expressions are analyzed by means of surveys for performing statistical inference [2]. By contrast, in _connectionist approaches_ a model is a network of interconnected simple processing units with knowledge embodied in the weights of the connections. Connections reflect local structural relationships that can result in dynamic global behavior. Similarly to statistical approaches, connectionist approaches develop models from observable data. However, with connectionist systems linguistic models are harder to observe, because the architectures are less constrained than statistical ones, so as to allow _emergent_ phenomena [9]. Both symbolic and statistical approaches belong to the cognitivist paradigm [9]. In this paradigm, the system is a descriptive product of a human designer, whose knowledge has to be explicitly formulated for a representational system of symbolic information processing. This designer-dependent representation biases the system, and constrains it to a consequence of the cognitive analysis of human activity. Indeed, it is well known that symbolic systems are highly context-dependent, neither scalable nor manageable [17], ineffectual in optimizing both grammar coverage and resultant ambiguity [17]. With respect to symbolic systems, statistical systems are more robust in the face of noisy and unexpected inputs, allowing broader coverage and being more adaptive [10]. Actually, every use of statistics is based upon a symbolic model, and statistics alone is not adequate for NLP [2; 10]. In contrast, connectionist systems can exhibit higher flexibility, by dynamically acquiring appropriate behavior on the given input, so as to be more robust and fault tolerant [10]. The connectionist approach discussed in this paper takes inspiration from the _emergent paradigm_[9], which reflects the dynamic sociological characteristics of natural languages.