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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Machine learning techniques have shown their competence for representing andreasoning in symbolic systems such as language and phonology. In SiniticHistorical Phonology, notable tasks that could benefit from machine learninginclude the comparison of dialects and reconstruction of proto-languagessystems. Motivated by this, this paper provides an approach for obtainingmulti-dialectal representations of Sinitic syllables, by constructing aknowledge graph from structured phonological data, then applying the BoxEtechnique from knowledge base learning. We applied unsupervised clusteringtechniques to the obtained representations to observe that the representationscapture phonemic contrast from the input dialects. Furthermore, we trainedclassifiers to perform inference of unobserved Middle Chinese labels, showingthe representations' potential for indicating archaic, proto-language features.The representations can be used for performing completion of fragmented Siniticphonological knowledge bases, estimating divergences between differentcharacters, or aiding the exploration and reconstruction of archaic features.

Multi-Dialectal Representation Learning of Sinitic Phonology