Abstract
This paper describes our participation as team FramingFreaks in the SemEval-2023 task 3 “Category and Framing Predictions in online news in a multi-lingual setup.” We participated in subtasks 1 and 2. Our approach was to classify texts by splitting them into subwords to reduce the feature set size and then using these tokens as input in Support Vector Machine (SVM) or logistic regression classifiers. Our results are similar to the baseline results.- Anthology ID:
- 2023.semeval-1.127
- Volume:
- Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Atul Kr. Ojha, A. Seza Doğruöz, Giovanni Da San Martino, Harish Tayyar Madabushi, Ritesh Kumar, Elisa Sartori
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 922–926
- Language:
- URL:
- https://aclanthology.org/2023.semeval-1.127
- DOI:
- 10.18653/v1/2023.semeval-1.127
- Cite (ACL):
- Rosina Baumann and Sabrina Deisenhofer. 2023. FramingFreaks at SemEval-2023 Task 3: Detecting the Category and the Framing of Texts as Subword Units with Traditional Machine Learning. In Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023), pages 922–926, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- FramingFreaks at SemEval-2023 Task 3: Detecting the Category and the Framing of Texts as Subword Units with Traditional Machine Learning (Baumann & Deisenhofer, SemEval 2023)
- PDF:
- https://preview.aclanthology.org/fix-volume-bibkeys/2023.semeval-1.127.pdf