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

Neural word representations have proven useful in Natural Language Processing(NLP) tasks due to their ability to efficiently model complex semantic andsyntactic word relationships. However, most techniques model only onerepresentation per word, despite the fact that a single word can have multiplemeanings or "senses". Some techniques model words by using multiple vectorsthat are clustered based on context. However, recent neural approaches rarelyfocus on the application to a consuming NLP algorithm. Furthermore, thetraining process of recent word-sense models is expensive relative tosingle-sense embedding processes. This paper presents a novel approach whichaddresses these concerns by modeling multiple embeddings for each word based onsupervised disambiguation, which provides a fast and accurate way for aconsuming NLP model to select a sense-disambiguated embedding. We demonstratethat these embeddings can disambiguate both contrastive senses such as nominaland verbal senses as well as nuanced senses such as sarcasm. We furtherevaluate Part-of-Speech disambiguated embeddings on neural dependency parsing,yielding a greater than 8% average error reduction in unlabeled attachmentscores across 6 languages.

sense2vec - A Fast and Accurate Method for Word Sense Disambiguation In Neural Word Embeddings