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

Deep neural networks employ multiple processing layers for learning textrepresentations to alleviate the burden of manual feature engineering inNatural Language Processing (NLP). Such text representations are widely used toextract features from unlabeled data. The word segmentation is a fundamentaland inevitable prerequisite for many languages. Sindhi is an under-resourcedlanguage, whose segmentation is challenging as it exhibits space omission,space insertion issues, and lacks the labeled corpus for segmentation. In thispaper, we investigate supervised Sindhi Word Segmentation (SWS) using unlabeleddata with a Subword Guided Neural Word Segmenter (SGNWS) for Sindhi. In orderto learn text representations, we incorporate subword representations torecurrent neural architecture to capture word information at morphemic-level,which takes advantage of Bidirectional Long-Short Term Memory (BiLSTM),self-attention mechanism, and Conditional Random Field (CRF). Our proposedSGNWS model achieves an F1 value of 98.51% without relying on featureengineering. The empirical results demonstrate the benefits of the proposedmodel over the existing Sindhi word segmenters.

A Subword Guided Neural Word Segmentation Model for Sindhi