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

Query expansion with pseudo-relevance feedback (PRF) is a powerful approachto enhance the effectiveness in information retrieval. Recently, with the rapidadvance of deep learning techniques, neural text generation has achievedpromising success in many natural language tasks. To leverage the strength oftext generation for information retrieval, in this article, we propose a novelapproach which effectively integrates text generation models into PRF-basedquery expansion. In particular, our approach generates augmented query termsvia neural text generation models conditioned on both the initial query andpseudo-relevance feedback. Moreover, in order to train the generative model, weadopt the conditional generative adversarial nets (CGANs) and propose thePRF-CGAN method in which both the generator and the discriminator areconditioned on the pseudo-relevance feedback. We evaluate the performance ofour approach on information retrieval tasks using two benchmark datasets. Theexperimental results show that our approach achieves comparable performance oroutperforms traditional query expansion methods on both the retrieval andreranking tasks.

GQE-PRF: Generative Query Expansion with Pseudo-Relevance Feedback