NELEC at SemEval-2019 Task 3: Think Twice Before Going Deep
Abstract
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
Cite
@article{arxiv.1904.03223,
title = {NELEC at SemEval-2019 Task 3: Think Twice Before Going Deep},
author = {Parag Agrawal and Anshuman Suri},
journal= {arXiv preprint arXiv:1904.03223},
year = {2019}
}
Comments
International Workshop on Semantic Evaluation (SemEval), NAACL-HLT 2019