Summary of Exploring Ordinality in Text Classification: a Comparative Study Of Explicit and Implicit Techniques, by Siva Rajesh Kasa et al.
Exploring Ordinality in Text Classification: A Comparative Study of Explicit and Implicit Techniques
by Siva Rajesh Kasa, Aniket Goel, Karan Gupta, Sumegh Roychowdhury, Anish Bhanushali, Nikhil Pattisapu, Prasanna Srinivasa Murthy
First submitted to arxiv on: 20 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a comprehensive study on Ordinal Classification (OC) in Natural Language Processing (NLP), focusing on sentiment analysis, rating prediction, and other applications. It compares and contrasts explicit and implicit approaches to tackle OC, leveraging Pretrained Language Models (PLMs). The work provides theoretical and empirical insights, as well as strategic recommendations for adopting the most effective approach depending on specific settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores ways to classify text with ordinal labels, like “positive”, “neutral”, or “negative”. It looks at two main methods: modifying existing loss functions to account for the order of labels, and using language models in a way that takes advantage of the implicit meaning of those labels. The research compares these approaches and provides guidance on which one is best to use in different situations. |
Keywords
» Artificial intelligence » Classification » Natural language processing » Nlp
