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

This paper proposes a model to learn word embeddings with weighted contextsbased on part-of-speech (POS) relevance weights. POS is a fundamental elementin natural language. However, state-of-the-art word embedding models fail toconsider it. This paper proposes to use position-dependent POS relevanceweighting matrices to model the inherent syntactic relationship among wordswithin a context window. We utilize the POS relevance weights to model eachword-context pairs during the word embedding training process. The modelproposed in this paper paper jointly optimizes word vectors and the POSrelevance matrices. Experiments conducted on popular word analogy and wordsimilarity tasks all demonstrated the effectiveness of the proposed method.

Part-of-Speech Relevance Weights for Learning Word Embeddings