Elizabeth Bear


pdf bib
Automatic annotation of curricular language targets to enrich activity models and support both pedagogy and adaptive systems
Martí Quixal | Björn Rudzewitz | Elizabeth Bear | Detmar Meurers
Proceedings of the 10th Workshop on NLP for Computer Assisted Language Learning


pdf bib
TueMix at SemEval-2020 Task 9: Logistic Regression with Linguistic Feature Set
Elizabeth Bear | Diana Constantina Hoefels | Mihai Manolescu
Proceedings of the Fourteenth Workshop on Semantic Evaluation

Commonly occurring in settings such as social media platforms, code-mixed content makes the task of identifying sentiment notably more challenging and complex due to the lack of structure and noise present in the data. SemEval-2020 Task 9, SentiMix, was organized with the purpose of detecting the sentiment of a given code-mixed tweet comprising Hindi and English. We tackled this task by comparing the performance of a system, TueMix - a logistic regression algorithm trained with three feature components: TF-IDF n-grams, monolingual sentiment lexicons, and surface features - with a neural network approach. Our results showed that TueMix outperformed the neural network approach and yielded a weighted F1-score of 0.685.