Elizabeth Bear

Fixing paper assignments

  1. Please select all papers that belong to the same person.
  2. Indicate below which author they should be assigned to.
Provide a valid ORCID iD here. This will be used to match future papers to this author.
Provide the name of the school or the university where the author has received or will receive their highest degree (e.g., Ph.D. institution for researchers, or current affiliation for students). This will be used to form the new author page ID, if needed.

TODO: "submit" and "cancel" buttons here

2021

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

2020

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.
Search
Co-authors
Venues
Fix data