Abstract
This paper describes the system we submitted to Task 10 (Capturing Discriminative Attributes) in SemEval 2018. Given a triple (word1, word2, attribute), this task is to predict whether it exemplifies a semantic difference or not. We design and investigate several word embedding features, PMI features and WordNet features together with supervised machine learning methods to address this task. Officially released results show that our system ranks above average.- Anthology ID:
- S18-1165
- Volume:
- Proceedings of the 12th International Workshop on Semantic Evaluation
- Month:
- June
- Year:
- 2018
- Address:
- New Orleans, Louisiana
- Editors:
- Marianna Apidianaki, Saif M. Mohammad, Jonathan May, Ekaterina Shutova, Steven Bethard, Marine Carpuat
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 999–1002
- Language:
- URL:
- https://aclanthology.org/S18-1165
- DOI:
- 10.18653/v1/S18-1165
- Cite (ACL):
- Yunxiao Zhou, Man Lan, and Yuanbin Wu. 2018. ECNU at SemEval-2018 Task 10: Evaluating Simple but Effective Features on Machine Learning Methods for Semantic Difference Detection. In Proceedings of the 12th International Workshop on Semantic Evaluation, pages 999–1002, New Orleans, Louisiana. Association for Computational Linguistics.
- Cite (Informal):
- ECNU at SemEval-2018 Task 10: Evaluating Simple but Effective Features on Machine Learning Methods for Semantic Difference Detection (Zhou et al., SemEval 2018)
- PDF:
- https://preview.aclanthology.org/improve-issue-templates/S18-1165.pdf