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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2405.11775 |
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| _version_ | 1866913356955254784 |
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| author | Kasa, Siva Rajesh Goel, Aniket Gupta, Karan Roychowdhury, Sumegh Bhanushali, Anish Pattisapu, Nikhil Murthy, Prasanna Srinivasa |
| author_facet | Kasa, Siva Rajesh Goel, Aniket Gupta, Karan Roychowdhury, Sumegh Bhanushali, Anish Pattisapu, Nikhil Murthy, Prasanna Srinivasa |
| contents | Ordinal Classification (OC) is a widely encountered challenge in Natural Language Processing (NLP), with applications in various domains such as sentiment analysis, rating prediction, and more. Previous approaches to tackle OC have primarily focused on modifying existing or creating novel loss functions that \textbf{explicitly} account for the ordinal nature of labels. However, with the advent of Pretrained Language Models (PLMs), it became possible to tackle ordinality through the \textbf{implicit} semantics of the labels as well. This paper provides a comprehensive theoretical and empirical examination of both these approaches. Furthermore, we also offer strategic recommendations regarding the most effective approach to adopt based on specific settings. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_11775 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Exploring Ordinality in Text Classification: A Comparative Study of Explicit and Implicit Techniques Kasa, Siva Rajesh Goel, Aniket Gupta, Karan Roychowdhury, Sumegh Bhanushali, Anish Pattisapu, Nikhil Murthy, Prasanna Srinivasa Computation and Language Machine Learning Ordinal Classification (OC) is a widely encountered challenge in Natural Language Processing (NLP), with applications in various domains such as sentiment analysis, rating prediction, and more. Previous approaches to tackle OC have primarily focused on modifying existing or creating novel loss functions that \textbf{explicitly} account for the ordinal nature of labels. However, with the advent of Pretrained Language Models (PLMs), it became possible to tackle ordinality through the \textbf{implicit} semantics of the labels as well. This paper provides a comprehensive theoretical and empirical examination of both these approaches. Furthermore, we also offer strategic recommendations regarding the most effective approach to adopt based on specific settings. |
| title | Exploring Ordinality in Text Classification: A Comparative Study of Explicit and Implicit Techniques |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2405.11775 |