Abstract
We present our contribution to the SemEval 22 Share Task 8: Multilingual news article similarity. The approach is lightweight and language-agnostic, it is based on the computation of several lexicographic and embedding-based features, and the use of a simple ML approach: random forests. In a notable departure from the task formulation, which is a ranking task, we tackled this task as a classification one. We present a detailed analysis of the behaviour of our system under different settings.- Anthology ID:
- 2022.semeval-1.166
- Volume:
- Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
- Month:
- July
- Year:
- 2022
- Address:
- Seattle, United States
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1178–1183
- Language:
- URL:
- https://aclanthology.org/2022.semeval-1.166
- DOI:
- 10.18653/v1/2022.semeval-1.166
- Cite (ACL):
- Nicolas Stefanovitch. 2022. Team TMA at SemEval-2022 Task 8: Lightweight and Language-Agnostic News Similarity Classifier. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), pages 1178–1183, Seattle, United States. Association for Computational Linguistics.
- Cite (Informal):
- Team TMA at SemEval-2022 Task 8: Lightweight and Language-Agnostic News Similarity Classifier (Stefanovitch, SemEval 2022)
- PDF:
- https://preview.aclanthology.org/paclic-22-ingestion/2022.semeval-1.166.pdf