Summary of A Study on Bias Detection and Classification in Natural Language Processing, by Ana Sofia Evans et al.
A Study on Bias Detection and Classification in Natural Language Processing
by Ana Sofia Evans, Helena Moniz, Luísa Coheur
First submitted to arxiv on: 14 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposed study aims to address the lack of publicly-available datasets for hate speech detection and classification by combining existing resources and analyzing their limitations. The research focuses on Natural Language Processing (NLP) and explores how human biases influence model performance. By developing experiments that combine different datasets, the study demonstrates that these combinations significantly impact model accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The goal is to create a better system for detecting hate speech online. This means gathering existing data sets and seeing how they work together to train models. The problem is that many of these data sets are small or biased, which can make it hard to get accurate results. |
Keywords
» Artificial intelligence » Classification » Natural language processing » Nlp
