Estimating the average need of semantic knowledge from distributional semantic models.
Continuous bag of words (CBOW) and skip-gram are two recently developed models of lexical semantics (Mikolov, Chen, Corrado, & Dean, Advances in Neural Information Processing Systems, 26, 3111-3119, 2013). Each has been demonstrated to perform markedly better at capturing human judgments about seman...
| Publicado en: | Memory & Cognition Vol. 45; no. 8; pp. 1350 - 1371 |
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| Formato: | Artículo |
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Springer Nature
Nov2017
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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=126091107&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 126091107 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 0090502X MEG jtl: Memory & Cognition issn: 0090502X maglogo: N pubinfo: dt: Nov2017 vid: 45 iid: 8 pid: 237 pub: Springer Nature artinfo: ui: 126091107 10.3758/s13421-017-0732-1 ppf: 1350 ppct: 21 formats: fmt: @attributes: type: P size: 827KB tig: atl: Estimating the average need of semantic knowledge from distributional semantic models. aug: au: Hollis, Geoff affil: Department of Psychology , University of Alberta , P217 Biological Sciences Building Edmonton T6G 2E9 Canada su: Cognition Intellect Learning Psychology Semantics Algorithms Mathematical models Theory Phonological awareness Latent semantic analysis sug: subj: Cognition Intellect Learning Psychology Semantics Algorithms Mathematical models Theory Phonological awareness Latent semantic analysis keyword: average need CBOW Contextual diversity Likely need Needs probability Rational analysis Skip-gram Word frequency average need CBOW Contextual diversity Likely need Needs probability Rational analysis Skip-gram Word frequency ab: Continuous bag of words (CBOW) and skip-gram are two recently developed models of lexical semantics (Mikolov, Chen, Corrado, & Dean, Advances in Neural Information Processing Systems, 26, 3111-3119, 2013). Each has been demonstrated to perform markedly better at capturing human judgments about semantic relatedness than competing models (e.g., latent semantic analysis; Landauer & Dumais, Psychological Review, 104(2), 1997 211; hyperspace analogue to language; Lund & Burgess, Behavior Research Methods, Instruments, & Computers, 28(2), 203-208, 1996). The new models were largely developed to address practical problems of meaning representation in natural language processing. Consequently, very little attention has been paid to the psychological implications of the performance of these models. We describe the relationship between the learning algorithms employed by these models and Anderson's rational theory of memory (J. R. Anderson & Milson, Psychological Review, 96(4), 703, 1989) and argue that CBOW is learning word meanings according to Anderson's concept of needs probability. We also demonstrate that CBOW can account for nearly all of the variation in lexical access measures typically attributable to word frequency and contextual diversity-two measures that are conceptually related to needs probability. These results suggest two conclusions: One, CBOW is a psychologically plausible model of lexical semantics. Two, word frequency and contextual diversity do not capture learning effects but rather memory retrieval effects. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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