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

• DOI: 10.18653/v1/s19-2045
• Publication Date: 2019-07-21T13:29:51Z
• Authors: Parag Agrawal; Anshuman Suri
• Keywords: FOS: Computer and information sciences; Computer Science - Computation and Language; 0202 electrical engineering, electronic engineering, information engineering; 02 engineering and technology; Computation and Language (cs.CL); Information Retrieval (cs.IR); Computer Science - Information Retrieval

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<br/>International Workshop on Semantic Evaluation (SemEval), NAACL-HLT 2019<br/>