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

Entity Resolution (ER) aims to identify whether two tuples refer to the samereal-world entity and is well-known to be labor-intensive. It is a prerequisiteto anomaly detection, as comparing the attribute values of two matched tuplesfrom two different datasets provides one effective way to detect anomalies.Existing ER approaches, due to insufficient feature discovery or error-proneinherent characteristics, are not able to achieve stable performance. In thispaper, we present CollaborER, a self-supervised entity resolution framework viamulti-features collaboration. It is capable of (i) obtaining reliable ERresults with zero human annotations and (ii) discovering adequate tuples'features in a fault-tolerant manner. CollaborER consists of two phases, i.e.,automatic label generation (ALG) and collaborative ER training (CERT). In thefirst phase, ALG is proposed to generate a set of positive tuple pairs and aset of negative tuple pairs. ALG guarantees the high quality of the generatedtuples and hence ensures the training quality of the subsequent CERT. In thesecond phase, CERT is introduced to learn the matching signals by discoveringgraph features and sentence features of tuples collaboratively. Extensiveexperimental results over eight real-world ER benchmarks show that CollaborERoutperforms all the existing unsupervised ER approaches and is comparable oreven superior to the state-of-the-art supervised ER methods.

CollaborER: A Self-supervised Entity Resolution Framework Using Multi-features Collaboration