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

Most evaluations for vector space models are semantically motivated, e.g. by measuring how well they capture word similarity. If one is interested in syntax-related downstream applications such as dependency parsing, a syntactically motivated evaluation seems preferable. As we show, the choice of embeddings has a noticeable impact on parser performance. Since evaluating embeddings directly in a parser is costly, we analyze the correlation between the full parsing task and a simple linear classification task as a potential proxy.

Evaluating Embeddings using Syntax-based Classification Tasks as a Proxy for Parser Performance