Abstract
This paper describes the system that we submitted for SemEval-2018 task 10: capturing discriminative attributes. Our system is built upon a simple idea of measuring the attribute word’s similarity with each of the two semantically similar words, based on an extended word embedding method and WordNet. Instead of computing the similarities between the attribute and semantically similar words by using standard word embeddings, we propose a novel method that combines word and context embeddings which can better measure similarities. Our model is simple and effective, which achieves an average F1 score of 0.62 on the test set.- Anthology ID:
- S18-1169
- 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:
- 1017–1021
- Language:
- URL:
- https://aclanthology.org/S18-1169
- DOI:
- 10.18653/v1/S18-1169
- Cite (ACL):
- Rui Mao, Guanyi Chen, Ruizhe Li, and Chenghua Lin. 2018. ABDN at SemEval-2018 Task 10: Recognising Discriminative Attributes using Context Embeddings and WordNet. In Proceedings of the 12th International Workshop on Semantic Evaluation, pages 1017–1021, New Orleans, Louisiana. Association for Computational Linguistics.
- Cite (Informal):
- ABDN at SemEval-2018 Task 10: Recognising Discriminative Attributes using Context Embeddings and WordNet (Mao et al., SemEval 2018)
- PDF:
- https://preview.aclanthology.org/improve-issue-templates/S18-1169.pdf