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

Learning algorithms normally assume that there is at most one annotation orlabel per data point. However, in some scenarios, such as medical diagnosis andon-line collaboration,multiple annotations may be available. In either case,obtaining labels for data points can be expensive and time-consuming (in somecircumstances ground-truth may not exist). Semi-supervised learning approacheshave shown that utilizing the unlabeled data is often beneficial in thesecases. This paper presents a probabilistic semi-supervised model and algorithmthat allows for learning from both unlabeled and labeled data in the presenceof multiple annotators. We assume that it is known what annotator labeled whichdata points. The proposed approach produces annotator models that allow us toprovide (1) estimates of the true label and (2) annotator variable expertisefor both labeled and unlabeled data. We provide numerical comparisons undervarious scenarios and with respect to standard semi-supervised learning.Experiments showed that the presented approach provides clear advantages overmulti-annotator methods that do not use the unlabeled data and over methodsthat do not use multi-labeler information.

Modeling Multiple Annotator Expertise in the Semi-Supervised Learning Scenario