Summary of Surfing the Modeling Of Pos Taggers in Low-resource Scenarios, by Manuel Vilares Ferro et al.
Surfing the modeling of PoS taggers in low-resource scenarios
by Manuel Vilares Ferro, Víctor M. Darriba Bilbao, Francisco J. Ribadas-Pena, Jorge Graña Gil
First submitted to arxiv on: 4 Feb 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 This research paper investigates the effectiveness of traditional machine learning algorithms in natural language processing, particularly in low-resource settings. The authors evaluate the early estimation of learning curves as a practical method for selecting the most suitable model in scenarios where deep learners may not be feasible. They use a formal approximation model to study the reliability of this approach in a resource-scarce environment, with a case study on generating Part-of-Speech (PoS) taggers for Galician language. The results support their expectations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at old machine learning methods and how they can still be useful today. It’s like checking if a classic car is still good even though there are newer models. In this case, the authors want to see if some older computer algorithms can help with tasks like understanding language, especially when we don’t have a lot of data or computing power. They tested one way to figure out which algorithm to use and found that it worked as expected. |
Keywords
* Artificial intelligence * Machine learning * Natural language processing
