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

Attribute values of the products are an essential component in any e-commerceplatform. Attribute Value Extraction (AVE) deals with extracting the attributesof a product and their values from its title or description. In this paper, wepropose to tackle the AVE task using generative frameworks. We present twotypes of generative paradigms, namely, word sequence-based and positionalsequence-based, by formulating the AVE task as a generation problem. We conductexperiments on two datasets where the generative approaches achieve the newstate-of-the-art results. This shows that we can use the proposed framework forAVE tasks without additional tagging or task-specific model design.

Exploring Generative Models for Joint Attribute Value Extraction from Product Titles