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

摘要

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 emoti-cons, 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 (Chatterjee et al., 2019b). Our system performs significantly better than the baseline, as well as our deep-learning model benchmarks. It achieved a micro-averaged F_1 score of 0.7765, ranking 3rd on the test-set leader-board.