Investigating the Phonetic Organisation of the English Language via Phonological Networks, Percolation and Markov Models

Stella, Massimo; Brede, Markus

doi:10.1007/978-3-319-29228-1_19

Cited by 8 publications

(9 citation statements)

References 20 publications

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“…Note also from Table 5 that no matter what effect

has on the degree distribution of the resulting PNN, all graphs show high clustering coefficients, short mean free paths, and high degree assortativity. The size of the giant component is much more variable, which agrees with previous work on the English PNN showing the second moment of the form length distribution strongly influences the giant component size [ 29 ] (note the difference in giant component size between ZTP(1x) and ZTP(1.5x)).…”

Section: Pseudolexiconssupporting

confidence: 88%

Universal Features in Phonological Neighbor Networks

Brown

Allopenna

Hunt

et al. 2018

Entropy

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Human speech perception involves transforming a countinuous acoustic signal into discrete linguistically meaningful units (phonemes) while simultaneously causing a listener to activate words that are similar to the spoken utterance and to each other. The Neighborhood Activation Model posits that phonological neighbors (two forms [words] that differ by one phoneme) compete significantly for recognition as a spoken word is heard. This definition of phonological similarity can be extended to an entire corpus of forms to produce a phonological neighbor network (PNN). We study PNNs for five languages: English, Spanish, French, Dutch, and German. Consistent with previous work, we find that the PNNs share a consistent set of topological features. Using an approach that generates random lexicons with increasing levels of phonological realism, we show that even random forms with minimal relationship to any real language, combined with only the empirical distribution of language-specific phonological form lengths, are sufficient to produce the topological properties observed in the real language PNNs. The resulting pseudo-PNNs are insensitive to the level of lingustic realism in the random lexicons but quite sensitive to the shape of the form length distribution. We therefore conclude that "universal" features seen across multiple languages are really string universals, not language universals, and arise primarily due to limitations in the kinds of networks generated by the one-step neighbor definition. Taken together, our results indicate that caution is warranted when linking the dynamics of human spoken word recognition to the topological properties of PNNs, and that the investigation of alternative similarity metrics for phonological forms should be a priority.

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“…Note also from Table 5 that no matter what effect

Section: Pseudolexiconssupporting

confidence: 88%

Universal Features in Phonological Neighbor Networks

Brown

Allopenna

Hunt

et al. 2018

Entropy

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“…Measurements of retrieval times 4 5 14 , age of acquisition 15 16 17 , creativity 18 and even semantic degradation due to ageing 19 have been studied and modeled using single layer networks. Further, networks of phonological word similarities have highlighted an upper bound on the size of phonological neighbourhoods as well as a tendency to avoid local clustering 20 21 22 . Experiments with adults have indicated that these constraints relate to word confusability in identification tasks 20 23 .…”

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confidence: 99%

Multiplex lexical networks reveal patterns in early word acquisition in children

Stella

Beckage

Brede

2017

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118

224

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Network models of language have provided a way of linking cognitive processes to language structure. However, current approaches focus only on one linguistic relationship at a time, missing the complex multi-relational nature of language. In this work, we overcome this limitation by modelling the mental lexicon of English-speaking toddlers as a multiplex lexical network, i.e. a multi-layered network where N = 529 words/nodes are connected according to four relationship: (i) free association, (ii) feature sharing, (iii) co-occurrence, and (iv) phonological similarity. We investigate the topology of the resulting multiplex and then proceed to evaluate single layers and the full multiplex structure on their ability to predict empirically observed age of acquisition data of English speaking toddlers. We find that the multiplex topology is an important proxy of the cognitive processes of acquisition, capable of capturing emergent lexicon structure. In fact, we show that the multiplex structure is fundamentally more powerful than individual layers in predicting the ordering with which words are acquired. Furthermore, multiplex analysis allows for a quantification of distinct phases of lexical acquisition in early learners: while initially all the multiplex layers contribute to word learning, after about month 23 free associations take the lead in driving word acquisition.

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“…The above results provide compelling evidence for the importance of multiplexity in predicting early word acquisition (cf. [19,20,49]), thus motivating the choice of coupling psycholinguistic features with network metrics in the current investigation.…”

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confidence: 99%

Modelling Early Word Acquisition through Multiplex Lexical Networks and Machine Learning

Stella¹

2019

BDCC

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Early language acquisition is a complex cognitive task. Recent data-informed approaches showed that children do not learn words uniformly at random but rather follow specific strategies based on the associative representation of words in the mental lexicon, a conceptual system enabling human cognitive computing. Building on this evidence, the current investigation introduces a combination of machine learning techniques, psycholinguistic features (i.e., frequency, length, polysemy and class) and multiplex lexical networks, representing the semantics and phonology of the mental lexicon, with the aim of predicting normative acquisition of 529 English words by toddlers between 22 and 26 months. Classifications using logistic regression and based on four psycholinguistic features achieve the best baseline cross-validated accuracy of 61.7% when half of the words have been acquired. Adding network information through multiplex closeness centrality enhances accuracy (up to 67.7%) more than adding multiplex neighbourhood density/degree (62.4%) or multiplex PageRank versatility (63.0%) or the best single-layer network metric, i.e., free association degree (65.2%), instead. Multiplex closeness operationalises the structural relevance of words for semantic and phonological information flow. These results indicate that the whole, global, multi-level flow of information and structure of the mental lexicon influence word acquisition more than single-layer or local network features of words when considered in conjunction with language norms. The highlighted synergy of multiplex lexical structure and psycholinguistic norms opens new ways for understanding human cognition and language processing through powerful and data-parsimonious cognitive computing approaches.

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Investigating the Phonetic Organisation of the English Language via Phonological Networks, Percolation and Markov Models

Cited by 8 publications

References 20 publications

Universal Features in Phonological Neighbor Networks

Universal Features in Phonological Neighbor Networks

Multiplex lexical networks reveal patterns in early word acquisition in children

Modelling Early Word Acquisition through Multiplex Lexical Networks and Machine Learning

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