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Rank over Class: The Untapped Potential of Ranking in Natural Language Processing

Atapour-Abarghouei, Amir; Bonner, Stephen; McGough, Andrew Stephen

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Stephen Bonner

Andrew Stephen McGough


Text classification has long been a staple within Natural Language Processing (NLP) with applications spanning across diverse areas such as sentiment analysis, recommender systems and spam detection. With such a powerful solution, it is often tempting to use it as the go-to tool for all NLP problems since when you are holding a hammer, everything looks like a nail. However, we argue here that many tasks which are currently addressed using classification are in fact being shoehorned into a classification mould and that if we instead address them as a ranking problem, we not only improve the model, but we achieve better performance. We propose a novel end-to-end ranking approach consisting of a Transformer network responsible for producing representations for a pair of text sequences, which are in turn passed into a context aggregating network outputting ranking scores used to determine an ordering to the sequences based on some notion of relevance. We perform numerous experiments on publicly-available datasets and investigate the applications of ranking in problems often solved using classification. In an experiment on a heavily- skewed sentiment analysis dataset, converting ranking results to classification labels yields an approximately 22% improvement over state-of-the-art text classification, demonstrating the efficacy of text ranking over text classification in certain scenarios.


Atapour-Abarghouei, A., Bonner, S., & McGough, A. S. (2021). Rank over Class: The Untapped Potential of Ranking in Natural Language Processing. .

Conference Name 2021 IEEE International Conference on Big Data (IEEE BigData 2021)
Conference Location Orlando, FL, USA
Start Date Dec 15, 2021
End Date Dec 18, 2021
Acceptance Date Nov 14, 2021
Publication Date Dec 15, 2021
Deposit Date Dec 3, 2021
Publicly Available Date Dec 6, 2021
Publisher Institute of Electrical and Electronics Engineers
ISBN 9781665445993
Related Public URLs


Accepted Conference Proceeding (481 Kb)

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