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

Word2Vec is a prominent model for natural language processing (NLP) tasks.Similar inspiration is found in distributed embeddings for new state-of-the-art(SotA) deep neural networks. However, wrong combination of hyper-parameters canproduce poor quality vectors. The objective of this work is to empirically showoptimal combination of hyper-parameters exists and evaluate variouscombinations. We compare them with the released, pre-trained original word2vecmodel. Both intrinsic and extrinsic (downstream) evaluations, including namedentity recognition (NER) and sentiment analysis (SA) were carried out. Thedownstream tasks reveal that the best model is usually task-specific, highanalogy scores don't necessarily correlate positively with F1 scores and thesame applies to focus on data alone. Increasing vector dimension size after apoint leads to poor quality or performance. If ethical considerations to savetime, energy and the environment are made, then reasonably smaller corpora maydo just as well or even better in some cases. Besides, using a small corpus, weobtain better human-assigned WordSim scores, corresponding Spearman correlationand better downstream performances (with significance tests) compared to theoriginal model, trained on 100 billion-word corpus.

Word2Vec: Optimal Hyper-Parameters and Their Impact on NLP Downstream Tasks