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

In this paper, we present a framework for Question Difficulty and ExpertiseEstimation (QDEE) in Community Question Answering sites (CQAs) such as Yahoo!Answers and Stack Overflow, which tackles a fundamental challenge incrowdsourcing: how to appropriately route and assign questions to users withthe suitable expertise. This problem domain has been the subject of muchresearch and includes both language-agnostic as well as language conscioussolutions. We bring to bear a key language-agnostic insight: that users gainexpertise and therefore tend to ask as well as answer more difficult questionsover time. We use this insight within the popular competition (directed) graphmodel to estimate question difficulty and user expertise by identifying keyhierarchical structure within said model. An important and novel contributionhere is the application of "social agony" to this problem domain. Difficultylevels of newly posted questions (the cold-start problem) are estimated byusing our QDEE framework and additional textual features. We also propose amodel to route newly posted questions to appropriate users based on thedifficulty level of the question and the expertise of the user. Extensiveexperiments on real world CQAs such as Yahoo! Answers and Stack Overflow datademonstrate the improved efficacy of our approach over contemporarystate-of-the-art models. The QDEE framework also allows us to characterize userexpertise in novel ways by identifying interesting patterns and roles played bydifferent users in such CQAs.

QDEE: Question Difficulty and Expertise Estimation in Community Question Answering Sites