Abstract
Detection of lexico-semantic relations is one of the central tasks of computational semantics. Although some fundamental relations (e.g., hypernymy) are asymmetric, most existing models account for asymmetry only implicitly and use the same concept representations to support detection of symmetric and asymmetric relations alike. In this work, we propose the Dual Tensor model, a neural architecture with which we explicitly model the asymmetry and capture the translation between unspecialized and specialized word embeddings via a pair of tensors. Although our Dual Tensor model needs only unspecialized embeddings as input, our experiments on hypernymy and meronymy detection suggest that it can outperform more complex and resource-intensive models. We further demonstrate that the model can account for polysemy and that it exhibits stable performance across languages.- Anthology ID:
- D17-1185
- Volume:
- Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
- Month:
- September
- Year:
- 2017
- Address:
- Copenhagen, Denmark
- Editors:
- Martha Palmer, Rebecca Hwa, Sebastian Riedel
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1757–1767
- Language:
- URL:
- https://aclanthology.org/D17-1185
- DOI:
- 10.18653/v1/D17-1185
- Cite (ACL):
- Goran Glavaš and Simone Paolo Ponzetto. 2017. Dual Tensor Model for Detecting Asymmetric Lexico-Semantic Relations. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 1757–1767, Copenhagen, Denmark. Association for Computational Linguistics.
- Cite (Informal):
- Dual Tensor Model for Detecting Asymmetric Lexico-Semantic Relations (Glavaš & Ponzetto, EMNLP 2017)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/D17-1185.pdf