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

April 05, 2019 · Entered Twilight · 🏛 International Workshop on Semantic Evaluation

"Last commit was 6.0 years ago (≥5 year threshold)"

Evidence collected by the PWNC Scanner

Repo contents: .gitignore, .gitmodules, Baseline.ipynb, LSTM-regex.ipynb, LSTM.ipynb, README.md, baseline.py, emoji2vec, regex.py, testBaseline.config, utils.py

Authors Parag Agrawal, Anshuman Suri arXiv ID 1904.03223 Category cs.CL: Computation & Language Cross-listed cs.IR Citations 22 Venue International Workshop on Semantic Evaluation Repository https://github.com/iamgroot42/nelec ⭐ 13 Last Checked 1 month ago

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