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

Text data augmentation, i.e., the creation of new textual data from anexisting text, is challenging. Indeed, augmentation transformations should takeinto account language complexity while being relevant to the target NaturalLanguage Processing (NLP) task (e.g., Machine Translation, TextClassification). Initially motivated by an application of Business EmailCompromise (BEC) detection, we propose a corpus and task agnostic augmentationframework used as a service to augment English texts within our company. Ourproposal combines different methods, utilizing BERT language model, multi-stepback-translation and heuristics. We show that our augmentation frameworkimproves performances on several text classification tasks using publiclyavailable models and corpora as well as on a BEC detection task. We alsoprovide a comprehensive argumentation about the limitations of our augmentationframework.

Text Data Augmentation: Towards better detection of spear-phishing emails