Automatic Classification of National Health Service Feedback
نویسندگان
چکیده
Text datasets come in an abundance of shapes, sizes and styles. However, determining what factors limit classification accuracy remains a difficult task which is still the subject intensive research. Using challenging UK National Health Service (NHS) dataset, contains many characteristics known to increase complexity classification, we propose innovative pipeline. This pipeline switches between different text pre-processing, scoring techniques during execution. this flexible pipeline, high level has been achieved range datasets, attaining micro-averaged F1 score 93.30% on Reuters-21578 “ApteMod” corpus. An evaluation was carried out using variety complex compared against unsupervised clustering approach. The paper describes how impacted by unbalanced category distribution, rare use generic terms subjective nature manual human classification.
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ژورنال
عنوان ژورنال: Mathematics
سال: 2022
ISSN: ['2227-7390']
DOI: https://doi.org/10.3390/math10060983