Abstract
This paper reports the STS-UHH participation in the SemEval 2017 shared Task 1 of Semantic Textual Similarity (STS). Overall, we submitted 3 runs covering monolingual and cross-lingual STS tracks. Our participation involves two approaches: unsupervised approach, which estimates a word alignment-based similarity score, and supervised approach, which combines dependency graph similarity and coverage features with lexical similarity measures using regression methods. We also present a way on ensembling both models. Out of 84 submitted runs, our team best multi-lingual run has been ranked 12th in overall performance with correlation of 0.61, 7th among 31 participating teams.- Anthology ID:
- S17-2025
- Volume:
- Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
- Month:
- August
- Year:
- 2017
- Address:
- Vancouver, Canada
- Editors:
- Steven Bethard, Marine Carpuat, Marianna Apidianaki, Saif M. Mohammad, Daniel Cer, David Jurgens
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 175–179
- Language:
- URL:
- https://aclanthology.org/S17-2025
- DOI:
- 10.18653/v1/S17-2025
- Cite (ACL):
- Sarah Kohail, Amr Rekaby Salama, and Chris Biemann. 2017. STS-UHH at SemEval-2017 Task 1: Scoring Semantic Textual Similarity Using Supervised and Unsupervised Ensemble. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pages 175–179, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- STS-UHH at SemEval-2017 Task 1: Scoring Semantic Textual Similarity Using Supervised and Unsupervised Ensemble (Kohail et al., SemEval 2017)
- PDF:
- https://preview.aclanthology.org/revert-3132-ingestion-checklist/S17-2025.pdf