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

with the rapid development of the Internet, textual information is growing rapidly. So document retrieval which aims to find and organize relevant information in text collections is needed. With the availability of large scale inexpensive storage the amount of information stored by organizations will increase. Searching for information and deriving useful facts will become more cumbersome. How to extract a lot of information quickly and effectively has become the focus of current research and hot topics. The state of the art for traditional IR techniques is to find relevant documents depending on matching words in users' query with individual words in text collections. The problem with Content-based retrieval systems is that documents relevant to a users' query are not retrieved, and many unrelated or irrelevant materials are retrieved. In this paper information retrieval method is proposed based on LSI approach. Latent Semantic Indexing (LSI) model is a concept based retrieval method that exploits the idea of vector space model and singular value decomposition. The goal of this research is to evaluate the applicability of LSI technique for textual document search and retrieval.

Framework for Document Retrieval using Latent Semantic Indexing