# Duluth at SemEval-2019 Task 6: Lexical Approaches to Identify and Categorize Offensive Tweets

arXiv: https://arxiv.org/abs/2007.12949 · HTML: https://mlsanity.com/paper/2007.12949/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jul 2020.

## Built on by (most cited first, 4 of 4)

- [Hidden behind the obvious: misleading keywords and implicitly abusive language on social media](https://mlsanity.com/paper/2205.01374/) (2022)
- [UoB at SemEval-2021 Task 5: Extending Pre-Trained Language Models to Include Task and Domain-Specific Information for Toxic Span Prediction](https://mlsanity.com/paper/2110.03730/) (2021)
- [minOffense: Inter-Agreement Hate Terms for Stable Rules, Concepts, Transitivities, and Lattices](https://mlsanity.com/paper/2305.17984/) (2023)
- [Fine-grained Verbal Attack Detection via a Hierarchical Divide-and-Conquer Framework](https://mlsanity.com/paper/2601.06907/) (2026)

## Abstract (arXiv)

This paper describes the Duluth systems that participated in SemEval--2019 Task 6, Identifying and Categorizing Offensive Language in Social Media (OffensEval). For the most part these systems took traditional Machine Learning approaches that built classifiers from lexical features found in manually labeled training data. However, our most successful system for classifying a tweet as offensive (or not) was a rule-based black--list approach, and we also experimented with combining the training data from two different but related SemEval tasks. Our best systems in each of the three OffensEval tasks placed in the middle of the comparative evaluation, ranking 57th of 103 in task A, 39th of 75 in task B, and 44th of 65 in task C.