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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Background and rationale Latent semantic analysis (LSA) is considered to be an efficient text mining technique [1] but most approaches developed on this paradigm are based on adhoc principles. A systematic study on the parameters affecting the performance of LSA is expected to provide guidelines to objectively select the LSA model parameters in a way that is consistent with the data and the application. In this study, empirical analyses were conducted using a previously published 50 gene data set [2] to examine the effects of the following parameters (outlined in Figure 1): Parameters are: (i) stemming, stop-words and word counts (to discard abstract with not enough information), (ii) corpus content (e.g., abstracts with and without titles), (iii) inclusion or exclusion of the dc component or 1st Eigen vector (that adds bias to the model), (iv) objective criteria to choose the number of factors (Eigen vectors) to create the model, (v) information theoretic criteria to select features (words in the corpus) instead of considering complete set of features.

A systematic study on latent semantic analysis model parameters for mining biomedical literature