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

This paper describes the ICS PAS system which took part in CoNLL 2018 sharedtask on Multilingual Parsing from Raw Text to Universal Dependencies. Thesystem consists of jointly trained tagger, lemmatizer, and dependency parserwhich are based on features extracted by a biLSTM network. The system uses bothfully connected and dilated convolutional neural architectures. The novelty ofour approach is the use of an additional loss function, which reduces thenumber of cycles in the predicted dependency graphs, and the use ofself-training to increase the system performance. The proposed system, i.e. ICSPAS (Warszawa), ranked 3th/4th in the official evaluation obtaining thefollowing overall results: 73.02 (LAS), 60.25 (MLAS) and 64.44 (BLEX).

Semi-Supervised Neural System for Tagging, Parsing and Lematization