English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

A major target of linguistics and cognitive science has been to understandwhat class of learning systems can acquire the key structures of naturallanguage. Until recently, the computational requirements of language have beenused to argue that learning is impossible without a highly constrainedhypothesis space. Here, we describe a learning system that is maximallyunconstrained, operating over the space of all computations, and is able toacquire several of the key structures present natural language from positiveevidence alone. The model successfully acquires regular (e.g. $(ab)^n$),context-free (e.g. $a^n b^n$, $x x^R$), and context-sensitive (e.g.$a^nb^nc^n$, $a^nb^mc^nd^m$, $xx$) formal languages. Our approach develops theconcept of factorized programs in Bayesian program induction in order to helpmanage the complexity of representation. We show in learning, the modelpredicts several phenomena empirically observed in human grammar acquisitionexperiments.

One Model for the Learning of Language