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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Character identification is an entity-linking task that finds words referring to the same person among the nouns mentioned in a conversation and turns them into one entity. In this paper, we define a sequence-labeling problem to solve character identification, and propose an attention-based recurrent neural network (RNN) encoder–decoder model. The in-put document for character identification on multiparty dialogues consists of several conversations, which increase the length of the input sequence. The RNN encoder–decoder model suffers from poor performance when the length of the input sequence is long. To solve this problem, we propose applying position encoding and the self-matching network to the RNN encoder–decoder model. Our experimental results demonstrate that of the four models proposed, Model 2 showed an F1 score of 86.00% and a label accuracy of 85.10% at the scene-level.

KNU CI System at SemEval-2018 Task4: Character Identification by Solving Sequence-Labeling Problem