Three Design Principles of Language: The Search for Parsimony in Redundancy.
In this paper we present three design principles of language – experience, heterogeneity and redundancy – and present recent developments in a family of models incorporating them, namely Data-Oriented Parsing/Unsupervised Data-Oriented Parsing. Although the idea of some form of redundant storage has...
| Publicado en: | Language & Speech Vol. 56; no. 3; pp. 265 - 291 |
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| Autores principales: | , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
Sep2013
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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=ccm&AN=104223065&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104223065 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00238309 3YY jtl: Language & Speech issn: 00238309 maglogo: Y pubinfo: dt: Sep2013 vid: 56 iid: 3 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 104223065 90217184 90217184 10.1177/0023830913484897 104223065 ppf: 265 ppct: 26 formats: fmt: @attributes: type: P tig: atl: Three Design Principles of Language: The Search for Parsimony in Redundancy. aug: au: Beekhuizen, Barend Bod, Rens Zuidema, Willem sug: subj: Language Development Classification Theory Construction Cognition Linguistics Language Processing Methods Repeated Measures Learning Methods Grammar Probability Vocabulary Phonetics Hypothesis Decision Trees Utilization Data Analysis Methods Human Analytic Research Funding Source ab: In this paper we present three design principles of language – experience, heterogeneity and redundancy – and present recent developments in a family of models incorporating them, namely Data-Oriented Parsing/Unsupervised Data-Oriented Parsing. Although the idea of some form of redundant storage has become part and parcel of parsing technologies and usage-based linguistic approaches alike, the question how much of it is cognitively realistic and/or computationally optimally efficient is an open one. We argue that a segmentation-based approach (Bayesian Model Merging) combined with an all-subtrees approach reduces the number of rules needed to achieve an optimal performance, thus making the parser more efficient. At the same time, starting from unsegmented wholes comes closer to the acquisitional situation of a language learner, and thus adds to the cognitive plausibility of the model. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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