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

Question classification (QC) is a prime constituent of automated questionanswering system. The work presented here demonstrates that the combination ofmultiple models achieve better classification performance than those obtainedwith existing individual models for the question classification task inBengali. We have exploited state-of-the-art multiple model combinationtechniques, i.e., ensemble, stacking and voting, to increase QC accuracy.Lexical, syntactic and semantic features of Bengali questions are used for fourwell-known classifiers, namely Na\"{\i}ve Bayes, kernel Na\"{\i}ve Bayes, RuleInduction, and Decision Tree, which serve as our base learners. Single-layerquestion-class taxonomy with 8 coarse-grained classes is extended to two-layertaxonomy by adding 69 fine-grained classes. We carried out the experiments bothon single-layer and two-layer taxonomies. Experimental results confirmed thatclassifier combination approaches outperform single classifier classificationapproaches by 4.02% for coarse-grained question classes. Overall, the stackingapproach produces the best results for fine-grained classification and achieves87.79% of accuracy. The approach presented here could be used in otherIndo-Aryan or Indic languages to develop a question answering system.

Classifier Combination Approach for Question Classification for Bengali Question Answering System