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

Microblogging sites, like Twitter, have emerged as ubiquitous sources ofinformation. Two important tasks related to the automatic extraction andanalysis of information in Microblogs are Entity Mention Detection (EMD) andEntity Detection (ED). The state-of-the-art EMD systems aim to model thenon-literary nature of microblog text by training upon offline static datasets.They extract a combination of surface-level features -- orthographic, lexical,and semantic -- from individual messages for noisy text modeling and entityextraction. But given the constantly evolving nature of microblog streams,detecting all entity mentions from such varying yet limited context of shortmessages remains a difficult problem. To this end, we propose a framework namedEMD Globalizer, better suited for the execution of EMD learners on microblogstreams. It deviates from the processing of isolated microblog messages byexisting EMD systems, where learned knowledge from the immediate context of amessage is used to suggest entities. After an initial extraction of entitycandidates by an EMD system, the proposed framework leverages occurrence miningto find additional candidate mentions that are missed during this firstdetection. Aggregating the local contextual representations of these mentions,a global embedding is drawn from the collective context of an entity candidatewithin a stream. The global embeddings are then utilized to separate entitieswithin the candidates from false positives. All mentions of said entities fromthe stream are produced in the framework's final outputs. Our experiments showthat EMD Globalizer can enhance the effectiveness of all existing EMD systemsthat we tested (on average by 25.61%) with a small additional computationaloverhead.

Boosting Entity Mention Detection for Targetted Twitter Streams with Global Contextual Embeddings