Abstract
Hypernymy plays a fundamental role in many AI tasks like taxonomy learning, ontology learning, etc. This has motivated the development of many automatic identification methods for extracting this relation, most of which rely on word distribution. We present a novel model HyperBox to learn box embeddings for hypernym discovery. Given an input term, HyperBox retrieves its suitable hypernym from a target corpus. For this task, we use the dataset published for SemEval 2018 Shared Task on Hypernym Discovery. We compare the performance of our model on two specific domains of knowledge: medical and music. Experimentally, we show that our model outperforms existing methods on the majority of the evaluation metrics. Moreover, our model generalize well over unseen hypernymy pairs using only a small set of training data.- Anthology ID:
- 2022.lrec-1.652
- Volume:
- Proceedings of the Thirteenth Language Resources and Evaluation Conference
- Month:
- June
- Year:
- 2022
- Address:
- Marseille, France
- Editors:
- Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Jan Odijk, Stelios Piperidis
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association
- Note:
- Pages:
- 6069–6076
- Language:
- URL:
- https://aclanthology.org/2022.lrec-1.652
- DOI:
- Cite (ACL):
- Maulik Parmar and Apurva Narayan. 2022. HyperBox: A Supervised Approach for Hypernym Discovery using Box Embeddings. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 6069–6076, Marseille, France. European Language Resources Association.
- Cite (Informal):
- HyperBox: A Supervised Approach for Hypernym Discovery using Box Embeddings (Parmar & Narayan, LREC 2022)
- PDF:
- https://preview.aclanthology.org/corrections-2024-07/2022.lrec-1.652.pdf
- Data
- SemEval-2018 Task-9