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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Static word embeddings encode word associations, extensively utilized indownstream NLP tasks. Although prior studies have discussed the nature of suchword associations in terms of biases and lexical regularities captured, thevariation in word associations based on the embedding training procedureremains in obscurity. This work aims to address this gap by assessingattributive word associations across five different static word embeddingarchitectures, analyzing the impact of the choice of the model architecture,context learning flavor and training corpora. Our approach utilizes asemi-supervised clustering method to cluster annotated proper nouns andadjectives, based on their word embedding features, revealing underlyingattributive word associations formed in the embedding space, withoutintroducing any confirmation bias. Our results reveal that the choice of thecontext learning flavor during embedding training (CBOW vs skip-gram) impactsthe word association distinguishability and word embeddings' sensitivity todeviations in the training corpora. Moreover, it is empirically shown that evenwhen trained over the same corpora, there is significant inter-model disparityand intra-model similarity in the encoded word associations across differentword embedding models, portraying specific patterns in the way the embeddingspace is created for each embedding architecture.

Model Choices Influence Attributive Word Associations: A Semi-supervised Analysis of Static Word Embeddings