Vector representation based on a supervised codebook for Nepali documents classification.

ABSTRACT: Document representation with outlier tokens exacerbates the classification performance due to the uncertain orientation of such tokens. Most existing document representation methods in different languages including Nepali mostly ignore the strategies to filter them out from documents before learning their representations. In this article, we propose a novel document representation method based on a supervised codebook to represent the Nepali documents, where our codebook contains only semantic tokens without outliers. Our codebook is domain-specific as it is based on tokens in a given corpus that have higher similarities with the class labels in the corpus. Our method adopts a simple yet prominent representation method for each word, called probability-based word embedding. To show the eff

SUBMITTER: Sitaula C 

PROVIDER: S-EPMC7959666 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature