# NELEC at SemEval-2019 Task 3: Think Twice Before Going Deep

arXiv: https://arxiv.org/abs/1904.03223 · HTML: https://mlsanity.com/paper/1904.03223/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Apr 2019.

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

- [Deep Emotion Recognition in Textual Conversations: A Survey](https://mlsanity.com/paper/2211.09172/) (2022)
- [AER-LLM: Ambiguity-aware Emotion Recognition Leveraging Large Language Models](https://mlsanity.com/paper/2409.18339/) (2024)

## Abstract (arXiv)

Existing Machine Learning techniques yield close to human performance on text-based classification tasks. However, the presence of multi-modal noise in chat data such as emoticons, slang, spelling mistakes, code-mixed data, etc. makes existing deep-learning solutions perform poorly. The inability of deep-learning systems to robustly capture these covariates puts a cap on their performance. We propose NELEC: Neural and Lexical Combiner, a system which elegantly combines textual and deep-learning based methods for sentiment classification. We evaluate our system as part of the third task of 'Contextual Emotion Detection in Text' as part of SemEval-2019. Our system performs significantly better than the baseline, as well as our deep-learning model benchmarks. It achieved a micro-averaged F1 score of 0.7765, ranking 3rd on the test-set leader-board. Our code is available at https://github.com/iamgroot42/nelec