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

Distributed representations of words as real-valued vectors in a relativelylow-dimensional space aim at extracting syntactic and semantic features fromlarge text corpora. A recently introduced neural network, named word2vec(Mikolov et al., 2013a; Mikolov et al., 2013b), was shown to encode semanticinformation in the direction of the word vectors. In this brief report, it isproposed to use the length of the vectors, together with the term frequency, asmeasure of word significance in a corpus. Experimental evidence using adomain-specific corpus of abstracts is presented to support this proposal. Auseful visualization technique for text corpora emerges, where words are mappedonto a two-dimensional plane and automatically ranked by significance.

Measuring Word Significance using Distributed Representations of Words