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

Instead of using expensive manual annotations, researchers have proposed totrain named entity recognition (NER) systems using heuristic labeling rules.However, devising labeling rules is challenging because it often requires aconsiderable amount of manual effort and domain expertise. To alleviate thisproblem, we propose \textsc{GLaRA}, a graph-based labeling rule augmentationframework, to learn new labeling rules from unlabeled data. We first create agraph with nodes representing candidate rules extracted from unlabeled data.Then, we design a new graph neural network to augment labeling rules byexploring the semantic relations between rules. We finally apply the augmentedrules on unlabeled data to generate weak labels and train a NER model using theweakly labeled data. We evaluate our method on three NER datasets and find thatwe can achieve an average improvement of +20\% F1 score over the best baselinewhen given a small set of seed rules.

GLaRA: Graph-based Labeling Rule Augmentation for Weakly Supervised Named Entity Recognition