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 Matching (EM) aims at recognizing entity records that denote the samereal-world object. Neural EM models learn vector representation of entitydescriptions and match entities end-to-end. Though robust, these methodsrequire many resources for training, and lack of interpretability. In thispaper, we propose a novel EM framework that consists of HeterogeneousInformation Fusion (HIF) and Key Attribute Tree (KAT) Induction to decouplefeature representation from matching decision. Using self-supervised learningand mask mechanism in pre-trained language modeling, HIF learns the embeddingsof noisy attribute values by inter-attribute attention with unlabeled data.Using a set of comparison features and a limited amount of annotated data, KATInduction learns an efficient decision tree that can be interpreted bygenerating entity matching rules whose structure is advocated by domainexperts. Experiments on 6 public datasets and 3 industrial datasets show thatour method is highly efficient and outperforms SOTA EM models in most cases.Our codes and datasets can be obtained from https://github.com/THU-KEG/HIF-KAT.

Interpretable and Low-Resource Entity Matching via Decoupling Feature Learning from Decision Making