An interpretable method for automated classification of spoken transcripts and written text.

ABSTRACT: We investigate the differences between spoken language (in the form of radio show transcripts) and written language (Wikipedia articles) in the context of text classification. We present a novel, interpretable method for text classification, involving a linear classifier using a large set of n-gram features, and apply it to a newly generated data set with sentences originating either from spoken transcripts or written text. Our classifier reaches an accuracy less than 0.02 below that of a commonly used classifier (DistilBERT) based on deep neural networks (DNNs). Moreover, our classifier has an integrated measure of confidence, for assessing the reliability of a given classification. An online tool is provided for demonstrating our classifier, particularly its interpretable nature, which i

SUBMITTER: Wahde M 

PROVIDER: S-EPMC10157555 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature