Abstract
This paper describes the SemEval 2018 Task 10 on Capturing Discriminative Attributes. Participants were asked to identify whether an attribute could help discriminate between two concepts. For example, a successful system should determine that ‘urine’ is a discriminating feature in the word pair ‘kidney’, ‘bone’. The aim of the task is to better evaluate the capabilities of state of the art semantic models, beyond pure semantic similarity. The task attracted submissions from 21 teams, and the best system achieved a 0.75 F1 score.- Anthology ID:
- S18-1117
- Volume:
- Proceedings of the 12th International Workshop on Semantic Evaluation
- Month:
- June
- Year:
- 2018
- Address:
- New Orleans, Louisiana
- Venues:
- SemEval | *SEM
- SIGs:
- SIGLEX | SIGSEM
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 732–740
- Language:
- URL:
- https://aclanthology.org/S18-1117
- DOI:
- 10.18653/v1/S18-1117
- Cite (ACL):
- Alicia Krebs, Alessandro Lenci, and Denis Paperno. 2018. SemEval-2018 Task 10: Capturing Discriminative Attributes. In Proceedings of the 12th International Workshop on Semantic Evaluation, pages 732–740, New Orleans, Louisiana. Association for Computational Linguistics.
- Cite (Informal):
- SemEval-2018 Task 10: Capturing Discriminative Attributes (Krebs et al., SemEval-*SEM 2018)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/S18-1117.pdf