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

Search has for a long time been an important tool for users to retrieveinformation. Syntactic search is matching documents or objects containingspecific keywords like user-history, location, preference etc. to improve theresults. However, it is often possible that the query and the best answer haveno term or very less number of terms in common and syntactic search can notperform properly in such cases. Semantic search, on the other hand, resolvesthese issues but suffers from lack of annotation, absence of WordNet in case oflow resource languages. In this work, we have demonstrated an end to endprocedure to improve the performance of semantic search using semi-supervisedand unsupervised learning algorithms. An available Bengali repository waschosen to have seven types of semantic properties primarily to develop thesystem. Performance has been tested using Support Vector Machine, Naive Bayes,Decision Tree and Artificial Neural Network (ANN). Our system has achieved theefficiency to predict the correct semantics using knowledge base over the timeof learning. A repository containing around a million sentences, a product ofTDIL project of Govt. of India, was used to test our system at first instance.Then the testing has been done for other languages. Being a cognitive system itmay be very useful for improving user satisfaction in e-Governance orm-Governance in the multilingual environment and also for other applications.

A Novel Approach to Enhance the Performance of Semantic Search in Bengali using Neural Net and other Classification Techniques