Dr Amir Atapour-Abarghouei amir.atapour-abarghouei@durham.ac.uk
Assistant Professor
Rank over Class: The Untapped Potential of Ranking in Natural Language Processing
Atapour-Abarghouei, Amir; Bonner, Stephen; McGough, Andrew Stephen
Authors
Stephen Bonner
Andrew Stephen McGough
Abstract
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.
Citation
Atapour-Abarghouei, A., Bonner, S., & McGough, A. S. (2021). Rank over Class: The Untapped Potential of Ranking in Natural Language Processing. . https://doi.org/10.1109/bigdata52589.2021.9671386
Conference Name | 2021 IEEE International Conference on Big Data (IEEE BigData 2021) |
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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 |
DOI | https://doi.org/10.1109/bigdata52589.2021.9671386 |
Related Public URLs | https://arxiv.org/pdf/2009.05160.pdf |
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© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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