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/

Zendy Plus

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

Zendy Tools

Zendy Open

Word representation has always been an important research area in the historyof natural language processing (NLP). Understanding such complex text data isimperative, given that it is rich in information and can be used widely acrossvarious applications. In this survey, we explore different word representationmodels and its power of expression, from the classical to modern-daystate-of-the-art word representation language models (LMS). We describe avariety of text representation methods, and model designs have blossomed in thecontext of NLP, including SOTA LMs. These models can transform large volumes oftext into effective vector representations capturing the same semanticinformation. Further, such representations can be utilized by various machinelearning (ML) algorithms for a variety of NLP related tasks. In the end, thissurvey briefly discusses the commonly used ML and DL based classifiers,evaluation metrics and the applications of these word embeddings in differentNLP tasks.

A Comprehensive Survey on Word Representation Models: From Classical to State-Of-The-Art Word Representation Language Models