Multi-Context Term Embeddings: the Use Case of Corpus-based Term Set Expansion

Jonathan Mamou, Oren Pereg, Moshe Wasserblat, Ido Dagan


Abstract
In this paper, we present a novel algorithm that combines multi-context term embeddings using a neural classifier and we test this approach on the use case of corpus-based term set expansion. In addition, we present a novel and unique dataset for intrinsic evaluation of corpus-based term set expansion algorithms. We show that, over this dataset, our algorithm provides up to 5 mean average precision points over the best baseline.
Anthology ID:
W19-2013
Volume:
Proceedings of the 3rd Workshop on Evaluating Vector Space Representations for NLP
Month:
June
Year:
2019
Address:
Minneapolis, USA
Venue:
RepEval
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
95–101
Language:
URL:
https://aclanthology.org/W19-2013
DOI:
10.18653/v1/W19-2013
Bibkey:
Cite (ACL):
Jonathan Mamou, Oren Pereg, Moshe Wasserblat, and Ido Dagan. 2019. Multi-Context Term Embeddings: the Use Case of Corpus-based Term Set Expansion. In Proceedings of the 3rd Workshop on Evaluating Vector Space Representations for NLP, pages 95–101, Minneapolis, USA. Association for Computational Linguistics.
Cite (Informal):
Multi-Context Term Embeddings: the Use Case of Corpus-based Term Set Expansion (Mamou et al., RepEval 2019)
Copy Citation:
PDF:
https://aclanthology.org/W19-2013.pdf