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Modelling the Human Mental Lexicon via Percolation, Markov Chains and Multiplex


Dr. Massimo Stella

Professor/a organitzador/a

Manlio de Domenico


University of Southampton, UK


02-11-2016 12:00


Language is a complex system, with a hierarchical set of units interacting on several levels. At the level of individual words, psycholinguists conjecture that the interactions among words are encoded within the human mind in the so-called human mental lexicon (HML), i.e. a mental dictionary where words are stored together with their linguistic data. Empirical research has shown that the interaction patterns among words have an impact in learning, storing and retrieving words from the HML, hence the importance of considering the structure of such relationships through a network paradigm. In [1, 2] we proposed a series of quantitative null models for phonological networks (PNs), where nodes represent words and links represent phonological similarities (i.e. two phonetic transcriptions having edit distance one). Our null models, based on site percolation and Markov processes, suggest the presence of additional constraints in the assembly of real words, such as (i) the avoidance of large degrees, (ii) and the avoidance of triadic closure, which are both compatible with previous empirical findings about avoiding word confusability. In [3] we extended previous analyses of the HML by adopting a multiplex network framework, including (i) phonological similarities, (ii) synonyms and (iii) free associations. This multi-layered structure was used for investigating how phonological and semantic relationships influence word acquisition through a toy model of lexicon growth driven by the phonological level. When similar sounding words are preferentially learned, the lexicon grows according to the local structure of the whole multiplex, otherwise features of the semantic layers and frequency become predominant, instead. In [4] we introduced the framework of multiplex lexical networks, i.e. multiplex networks where nodes represent words connected differently on different layers but also endowed with exogenous features such as frequency and age of acquisition. We adopted the multiplex lexical networks of English toddlers for investigating and predicting their normative learning of words between age 18 and 30. Multiplex measures resulted into a higher predictive power of acquired words when compared to single-layer network measures or exogenous estimators such as frequency. [1] M. Stella and M. Brede, Patterns in the English language: phonological networks, percolation and assembly models, JSTAT, P05006 (2015). [2] M. Stella and M. Brede, Investigating the Phonetic Organisation of the English Language via Phonological Networks, Percolation and Markov Models, accepted in Proceedings of ECCS2014, Lecture Notes in Computer Science, Springer (2015). [3] M. Stella and M. Brede, Mental Lexicon Growth Modelling Reveals the Multiplexity of the English Language, Proceedings of the 7th Workshop on Complex Networks, Springer (2016). [4] M. Stella, N. Beckage and M. Brede, Multiplex lexical networks reveal patterns in early word acquisition in children, (2016).


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