Abstract
This paper describes the HHU system that participated in Task 2 of SemEval 2017, Multilingual and Cross-lingual Semantic Word Similarity. We introduce our unsupervised embedding learning technique and describe how it was employed and configured to address the problems of monolingual and multilingual word similarity measurement. This paper reports from empirical evaluations on the benchmark provided by the task’s organizers.- Anthology ID:
- S17-2039
- Volume:
- Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
- Month:
- August
- Year:
- 2017
- Address:
- Vancouver, Canada
- Editors:
- Steven Bethard, Marine Carpuat, Marianna Apidianaki, Saif M. Mohammad, Daniel Cer, David Jurgens
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 250–255
- Language:
- URL:
- https://aclanthology.org/S17-2039
- DOI:
- 10.18653/v1/S17-2039
- Cite (ACL):
- Behrang QasemiZadeh and Laura Kallmeyer. 2017. HHU at SemEval-2017 Task 2: Fast Hash-Based Embeddings for Semantic Word Similarity Assessment. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pages 250–255, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- HHU at SemEval-2017 Task 2: Fast Hash-Based Embeddings for Semantic Word Similarity Assessment (QasemiZadeh & Kallmeyer, SemEval 2017)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/S17-2039.pdf