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
- Editors:
- Anna Rogers, Aleksandr Drozd, Anna Rumshisky, Yoav Goldberg
- 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
- 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)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/W19-2013.pdf