# UBC-NLP at SemEval-2019 Task 6:Ensemble Learning of Offensive Content With Enhanced Training Data

arXiv: https://arxiv.org/abs/1906.03692 · HTML: https://mlsanity.com/paper/1906.03692/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jun 2019.

## Built on by (most cited first, 2 of 2)

- [EkoHate: Abusive Language and Hate Speech Detection for Code-switched Political Discussions on Nigerian Twitter](https://mlsanity.com/paper/2404.18180/) (2024)
- [Improved two-stage hate speech classification for twitter based on Deep Neural Networks](https://mlsanity.com/paper/2206.04162/) (2022)

## Abstract (arXiv)

We examine learning offensive content on Twitter with limited, imbalanced data. For the purpose, we investigate the utility of using various data enhancement methods with a host of classical ensemble classifiers. Among the 75 participating teams in SemEval-2019 sub-task B, our system ranks 6th (with 0.706 macro F1-score). For sub-task C, among the 65 participating teams, our system ranks 9th (with 0.587 macro F1-score).