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Analogical inference from distributional structure: What recurrent neural networks can tell us about word learning

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3AF3ZB8EHP" target="_blank" >RIV/00216208:11320/23:F3ZB8EHP - isvavai.cz</a>

  • Result on the web

    <a href="https://www.sciencedirect.com/science/article/pii/S2666827023000312" target="_blank" >https://www.sciencedirect.com/science/article/pii/S2666827023000312</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1016/j.mlwa.2023.100478" target="_blank" >10.1016/j.mlwa.2023.100478</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Analogical inference from distributional structure: What recurrent neural networks can tell us about word learning

  • Original language description

    "One proposal that can explain the remarkable pace of word learning in young children is that they leverage the language-internal distributional similarity of familiar and novel words to make analogical inferences about possible meanings of novel words"

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>ost</sub> - Miscellaneous article in a specialist periodical

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

  • Continuities

Others

  • Publication year

    2023

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Name of the periodical

    "Machine Learning with Applications"

  • ISSN

    2666-8270

  • e-ISSN

  • Volume of the periodical

    13

  • Issue of the periodical within the volume

    2023-2-28

  • Country of publishing house

    US - UNITED STATES

  • Number of pages

    34

  • Pages from-to

    1-34

  • UT code for WoS article

  • EID of the result in the Scopus database