Abstract
Hypernym discovery aims to discover the hypernym word sets given a hyponym word and proper corpus. This paper proposes a simple but effective method for the discovery of hypernym sets based on word embedding, which can be used to measure the contextual similarities between words. Given a test hyponym word, we get its hypernym lists by computing the similarities between the hyponym word and words in the training data, and fill the test word’s hypernym lists with the hypernym list in the training set of the nearest similarity distance to the test word. In SemEval 2018 task9, our results, achieve 1st on Spanish, 2nd on Italian, 6th on English in the metric of MAP.- Anthology ID:
- S18-1148
- 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:
- 909–913
- Language:
- URL:
- https://aclanthology.org/S18-1148
- DOI:
- 10.18653/v1/S18-1148
- Cite (ACL):
- Wei Qiu, Mosha Chen, Linlin Li, and Luo Si. 2018. NLP_HZ at SemEval-2018 Task 9: a Nearest Neighbor Approach. In Proceedings of the 12th International Workshop on Semantic Evaluation, pages 909–913, New Orleans, Louisiana. Association for Computational Linguistics.
- Cite (Informal):
- NLP_HZ at SemEval-2018 Task 9: a Nearest Neighbor Approach (Qiu et al., SemEval 2018)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/S18-1148.pdf
- Data
- SemEval-2018 Task-9