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

Word embeddings induced from local context are prevalent in NLP. A simple andeffective context-based multilingual embedding learner is Levy et al. (2017)'sS-ID (sentence ID) method. Another line of work induces high-performingmultilingual embeddings from concepts (Dufter et al., 2018). In this paper, wepropose Co+Co, a simple and scalable method that combines context-based andconcept-based learning. From a sentence aligned corpus, concepts are extractedvia sampling; words are then associated with their concept ID and sentence IDin embedding learning. This is the first work that successfully combinescontext-based and concept-based embedding learning. We show that Co+Co performswell for two different application scenarios: the Parallel Bible Corpus (1000+languages, low-resource) and EuroParl (12 languages, high-resource). Amongmethods applicable to both corpora, Co+Co performs best in our evaluation setupof six tasks.

Multilingual Embeddings Jointly Induced from Contexts and Concepts: Simple, Strong and Scalable