HHU at SemEval-2017 Task 2: Fast Hash-Based Embeddings for Semantic Word Similarity Assessment

Behrang QasemiZadeh, Laura Kallmeyer


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
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/S17-2039.pdf