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

This paper presents a method of designing specific high-order dependencyfactor on the linear chain conditional random fields (CRFs) for named entityrecognition (NER). Named entities tend to be separated from each other bymultiple outside tokens in a text, and thus the first-order CRF, as well as thesecond-order CRF, may innately lose transition information between distantnamed entities. The proposed design uses outside label in NER as a transmissionmedium of precedent entity information on the CRF. Then, empirical resultsapparently demonstrate that it is possible to exploit long-distance labeldependency in the original first-order linear chain CRF structure upon NERwhile reducing computational loss rather than in the second-order CRF.

Connecting Distant Entities with Induction through Conditional Random Fields for Named Entity Recognition: Precursor-Induced CRF