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 Tools annual

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 monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Distributed vector representations for natural language vocabulary get a lotof attention in contemporary computational linguistics. This paper summarizesthe experience of applying neural network language models to the task ofcalculating semantic similarity for Russian. The experiments were performed inthe course of Russian Semantic Similarity Evaluation track, where our modelstook from the 2nd to the 5th position, depending on the task.  We introduce the tools and corpora used, comment on the nature of the sharedtask and describe the achieved results. It was found out that ContinuousSkip-gram and Continuous Bag-of-words models, previously successfully appliedto English material, can be used for semantic modeling of Russian as well.Moreover, we show that texts in Russian National Corpus (RNC) provide anexcellent training material for such models, outperforming other, much largercorpora. It is especially true for semantic relatedness tasks (althoughstacking models trained on larger corpora on top of RNC models improvesperformance even more).  High-quality semantic vectors learned in such a way can be used in a varietyof linguistic tasks and promise an exciting field for further study.

Texts in, meaning out: neural language models in semantic similarity task for Russian