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

Named entity recognition (NER) is a fundamental task of natural languageprocessing (NLP). However, most state-of-the-art research is mainly oriented tohigh-resource languages such as English and has not been widely applied tolow-resource languages. In Malay language, relevant NER resources are limited.In this work, we propose a dataset construction framework, which is based onlabeled datasets of homologous languages and iterative optimization, to build aMalay NER dataset (MYNER) comprising 28,991 sentences (over 384 thousandtokens). Additionally, to better integrate boundary information for NER, wepropose a multi-task (MT) model with a bidirectional revision (Bi-revision)mechanism for Malay NER task. Specifically, an auxiliary task, boundarydetection, is introduced to improve NER training in both explicit and implicitways. Furthermore, a gated ignoring mechanism is proposed to conductconditional label transfer and alleviate error propagation by the auxiliarytask. Experimental results demonstrate that our model achieves comparableresults over baselines on MYNER. The dataset and the model in this paper wouldbe publicly released as a benchmark dataset.

An Open-Source Dataset and A Multi-Task Model for Malay Named Entity Recognition