src_fraser_4574 for A dynamic network analysis of emergent grammar

src_fraser_4574 for A dynamic network analysis of emergent grammar by Paul Ibbotson, Vsevolod Salnikov and Richard Walker in First Languag

src_fraser_2287 for A dynamic network analysis of emergent grammar

src_fraser_2287 for A dynamic network analysis of emergent grammar by Paul Ibbotson, Vsevolod Salnikov and Richard Walker in First Languag

src_eleanor_2061 for A dynamic network analysis of emergent grammar

src_eleanor_2061 for A dynamic network analysis of emergent grammar by Paul Ibbotson, Vsevolod Salnikov and Richard Walker in First Languag

src_eleanor_200 for A dynamic network analysis of emergent grammar

src_eleanor_200 for A dynamic network analysis of emergent grammar by Paul Ibbotson, Vsevolod Salnikov and Richard Walker in First Languag

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src_eleanor_4122 for A dynamic network analysis of emergent grammar by Paul Ibbotson, Vsevolod Salnikov and Richard Walker in First Languag

src_fraser_200 for A dynamic network analysis of emergent grammar

src_fraser_200 for A dynamic network analysis of emergent grammar by Paul Ibbotson, Vsevolod Salnikov and Richard Walker in First Languag

A dynamic network analysis of emergent grammar

For languages to survive as complex cultural systems, they need to be learnable. According to traditional approaches, learning is made possible by constraining the degrees of freedom in advance of experience and by the construction of complex structure during development. This article explores a third contributor to complexity: namely, the extent to which syntactic structure can be an emergent property of how simpler entities – words – interact with one another. The authors found that when naturalistic child directed speech was instantiated in a dynamic network, communities formed around words that were more densely connected with other words than they were with the rest of the network. This process is designed to mirror what we know about distributional patterns in natural language: namely, the network communities represented the syntactic hubs of semi-formulaic slot-and-frame patterns, characteristic of early speech. The network itself was blind to grammatical information and its organization reflected (a) the frequency of using a word and (b) the probabilities of transitioning from one word to another. The authors show that grammatical patterns in the input disassociate by community structure in the emergent network. These communities provide coherent hubs which could be a reliable source of syntactic information for the learner. These initial findings are presented here as proof-of-concept in the hope that other researchers will explore the possibilities and limitations of this approach on a larger scale and with more languages. The implications of a dynamic network approach are discussed for the learnability burden and the development of an adult-like grammar

Data from: The topology of a discussion: the #occupy case

Introduction: We analyse a large sample of the Twitter activity that developed around the social movement 'Occupy Wall Street', to study the complex interactions between the human communication activity and the semantic content of a debate. Methods: We use a network approach based on the analysis of the bipartite graph @Users-#Hashtags and of its projections: the 'semantic network', whose nodes are hashtags, and the 'users interest network', whose nodes are users. In the first instance, we find out that discussion topics (#hashtags) present a high structural heterogeneity, with a relevant role played by the semantic hubs that are responsible to guarantee the continuity of the debate. In the users’ case, the self-organisation process of users’ activity, leads to the emergence of two classes of communicators: the 'professionals' and the 'amateurs'. Results: Both the networks present a strong community structure, based on the differentiation of the semantic topics, and a high level of structural robustness when certain sets of topics are censored and/or accounts are removed. Conclusions: By analysing the characteristics of the dynamical networks we can distinguish three phases of the discussion about the movement. Each phase corresponds to a specific moment of the movement: from declaration of intent, organisation and development and the final phase of political reactions. Each phase is characterised by the presence of prototypical #hashtags in the discussion.Introduction: We analyse a large sample of the Twitter activity that developed around the social movement 'Occupy Wall Street', to study the complex interactions between the human communication activity and the semantic content of a debate. Methods: We use a network approach based on the analysis of the bipartite graph @Users-#Hashtags and of its projections: the 'semantic network', whose nodes are hashtags, and the 'users interest network', whose nodes are users. In the first instance, we find out that discussion topics (#hashtags) present a high structural heterogeneity, with a relevant role played by the semantic hubs that are responsible to guarantee the continuity of the debate. In the users’ case, the self-organisation process of users’ activity, leads to the emergence of two classes of communicators: the 'professionals' and the 'amateurs'. Results: Both the networks present a strong community structure, based on the differentiation of the semantic topics, and a high level of structural robustness when certain sets of topics are censored and/or accounts are removed. Conclusions: By analysing the characteristics of the dynamical networks we can distinguish three phases of the discussion about the movement. Each phase corresponds to a specific moment of the movement: from declaration of intent, organisation and development and the final phase of political reactions. Each phase is characterised by the presence of prototypical #hashtags in the discussion

src_eleanor_6184 for A dynamic network analysis of emergent grammar

src_eleanor_6184 for A dynamic network analysis of emergent grammar by Paul Ibbotson, Vsevolod Salnikov and Richard Walker in First Languag

MOESM1 of The classical origin of modern mathematics

The historical origin of modern science. (pdf