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/

Zendy Open

Presents the pdf analysis as premium feature

PDF Analysis

Insights

Presents the summarisation as premium feature

Summarisation

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Natural Language Understanding (NLU) models are typically trained in a supervised learning framework. In the case of intent classification, the predicted labels are predefined and based on the designed annotation schema while the labelling process is based on a laborious task where annotators manually inspect each utterance and assign the corresponding label. We propose an Active Annotation (AA) approach where we combine an unsupervised learning method in the embedding space, a human-in-the-loop verification process, and linguistic insights to create lexicons that can be open categories and adapted over time. In particular, annotators define the y-label space on-the-fly during the annotation using an iterative process and without the need for prior knowledge about the input data. We evaluate the proposed annotation paradigm in a real use-case NLU scenario. Results show that our Active Annotation paradigm achieves accurate and higher quality training data, with an annotation speed of an order of magnitude higher with respect to the traditional human-only driven baseline annotation methodology.

Active Annotation: Bootstrapping Annotation Lexicon and Guidelines for Supervised NLU Learning