Abstract
We present a simple and effective feed-forward neural architecture for discriminating between lexico-semantic relations (synonymy, antonymy, hypernymy, and meronymy). Our Specialization Tensor Model (STM) simultaneously produces multiple different specializations of input distributional word vectors, tailored for predicting lexico-semantic relations for word pairs. STM outperforms more complex state-of-the-art architectures on two benchmark datasets and exhibits stable performance across languages. We also show that, if coupled with a bilingual distributional space, the proposed model can transfer the prediction of lexico-semantic relations to a resource-lean target language without any training data.- Anthology ID:
- N18-2029
- Volume:
- Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)
- Month:
- June
- Year:
- 2018
- Address:
- New Orleans, Louisiana
- Editors:
- Marilyn Walker, Heng Ji, Amanda Stent
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 181–187
- Language:
- URL:
- https://aclanthology.org/N18-2029
- DOI:
- 10.18653/v1/N18-2029
- Cite (ACL):
- Goran Glavaš and Ivan Vulić. 2018. Discriminating between Lexico-Semantic Relations with the Specialization Tensor Model. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pages 181–187, New Orleans, Louisiana. Association for Computational Linguistics.
- Cite (Informal):
- Discriminating between Lexico-Semantic Relations with the Specialization Tensor Model (Glavaš & Vulić, NAACL 2018)
- PDF:
- https://preview.aclanthology.org/naacl24-info/N18-2029.pdf
- Code
- codogogo/stm