NELEC at SemEval-2019 Task 3: Think Twice Before Going Deep
Parag Agrawal, Anshuman Suri · 2019
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 deeplearning 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 (Chatterjee et al., 2019b).Our system performs significantly better than the baseline, as well as our deeplearning model benchmarks.It achieved a micro-averaged F 1 score of 0.7765, ranking 3 rd on the test-set leader-board.Our code is available at https://github.com/ iamgroot42/nelec