# NLPR@SRPOL at SemEval-2019 Task 6 and Task 5: Linguistically enhanced deep learning offensive sentence classifier

## TL;DR

This paper describes a linguistically enhanced deep learning ensemble system for offensive sentence classification, achieving second place in SemEval-2019 Task 6, by combining multiple models, embeddings, and linguistic features.

## Contribution

The paper introduces a novel ensemble approach integrating diverse models, embeddings, and linguistic features for improved offensive language detection.

## Key findings

- Ensemble system outperforms individual models.
- Linguistic features significantly improve accuracy.
- Combination of embeddings and features enhances performance.

## Abstract

The paper presents a system developed for the SemEval-2019 competition Task 5 hat-Eval Basile et al. (2019) (team name: LU Team) and Task 6 OffensEval Zampieri et al. (2019b) (team name: NLPR@SRPOL), where we achieved 2nd position in Subtask C. The system combines in an ensemble several models (LSTM, Transformer, OpenAI's GPT, Random forest, SVM) with various embeddings (custom, ELMo, fastText, Universal Encoder) together with additional linguistic features (number of blacklisted words, special characters, etc.). The system works with a multi-tier blacklist and a large corpus of crawled data, annotated for general offensiveness. In the paper we do an extensive analysis of our results and show how the combination of features and embedding affect the performance of the models.