What Can Graph Theory Tell Us About Word Learning and Lexical Retrieval?
Purpose: Graph theory and the new science ot networks provide a mathematically rigorous approach to examine the development and organization of complex systems. These tools were applied to the mental lexicon to examine the organization of words in the lexicon and to explore how that structure might...
| Publicado en: | Journal of Speech, Language & Hearing Research Vol. 51; no. 2; pp. 408 - 423 |
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| Formato: | Artículo |
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American Speech-Language-Hearing Association
April 2008
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=508056340&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 508056340 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 10924388 1SM jtl: Journal of Speech, Language & Hearing Research issn: 10924388 maglogo: N pubinfo: dt: April 2008 vid: 51 iid: 2 pid: 42 pub: American Speech-Language-Hearing Association artinfo: ui: 508056340 10.1044/1092-4388(2008/030) ppf: 408 ppct: 15 formats: fmt: – @attributes: type: T – @attributes: type: P size: 255KB tig: atl: What Can Graph Theory Tell Us About Word Learning and Lexical Retrieval? aug: au: Vitevitch, Michael S. su: Computational linguistics Graph theory Word recognition Verbal learning sug: subj: Computational linguistics Graph theory Word recognition Verbal learning ab: Purpose: Graph theory and the new science ot networks provide a mathematically rigorous approach to examine the development and organization of complex systems. These tools were applied to the mental lexicon to examine the organization of words in the lexicon and to explore how that structure might influence the acquisition and retrieval of phonological word-forms. Method: Pajek, a program for large network analysis and visualization (V. Batagelj & A. Mvrar, 1998), was used to examine several characteristics of a network derived from a computerized database of the adult lexicon. Nodes in the network represented words, and a link connected two nodes if the words were phonological neighbors. Results: The average path length and clustering coefficient suggest that the phonological network exhibits small-world characteristics. The degree distribution was fit better by an exponential rather than a power-law function. Finally, the network exhibited assortative mixing by degree. Some of these structural characteristics were also found in graphs that were formed by 2 simple stochastic processes suggesting that similar processes might influence the development of the lexicon. Conclusions: The graph theoretic perspective may provide novel insights about the mental lexicon and lead to future studies that help us better understand language development and processing. Reprinted by permission of the publisher. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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