Abstract
In this paper we present three unsupervised models for capturing discriminative attributes based on information from word embeddings, WordNet, and sentence-level word co-occurrence frequency. We show that, of these approaches, the simple approach based on word co-occurrence performs best. We further consider supervised and unsupervised approaches to combining information from these models, but these approaches do not improve on the word co-occurrence model.- Anthology ID:
- S18-1168
- 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:
- 1013–1016
- Language:
- URL:
- https://aclanthology.org/S18-1168
- DOI:
- 10.18653/v1/S18-1168
- Cite (ACL):
- Milton King, Ali Hakimi Parizi, and Paul Cook. 2018. UNBNLP at SemEval-2018 Task 10: Evaluating unsupervised approaches to capturing discriminative attributes. In Proceedings of the 12th International Workshop on Semantic Evaluation, pages 1013–1016, New Orleans, Louisiana. Association for Computational Linguistics.
- Cite (Informal):
- UNBNLP at SemEval-2018 Task 10: Evaluating unsupervised approaches to capturing discriminative attributes (King et al., SemEval 2018)
- PDF:
- https://preview.aclanthology.org/naacl24-info/S18-1168.pdf