Abstract
Recent studies on semantic frame induction show that relatively high performance has been achieved by using clustering-based methods with contextualized word embeddings. However, there are two potential drawbacks to these methods: one is that they focus too much on the superficial information of the frame-evoking verb and the other is that they tend to divide the instances of the same verb into too many different frame clusters. To overcome these drawbacks, we propose a semantic frame induction method using masked word embeddings and two-step clustering. Through experiments on the English FrameNet data, we demonstrate that using the masked word embeddings is effective for avoiding too much reliance on the surface information of frame-evoking verbs and that two-step clustering can improve the number of resulting frame clusters for the instances of the same verb.- Anthology ID:
- 2021.acl-short.102
- Volume:
- Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
- Month:
- August
- Year:
- 2021
- Address:
- Online
- Editors:
- Chengqing Zong, Fei Xia, Wenjie Li, Roberto Navigli
- Venues:
- ACL | IJCNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 811–816
- Language:
- URL:
- https://aclanthology.org/2021.acl-short.102
- DOI:
- 10.18653/v1/2021.acl-short.102
- Cite (ACL):
- Kosuke Yamada, Ryohei Sasano, and Koichi Takeda. 2021. Semantic Frame Induction using Masked Word Embeddings and Two-Step Clustering. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 811–816, Online. Association for Computational Linguistics.
- Cite (Informal):
- Semantic Frame Induction using Masked Word Embeddings and Two-Step Clustering (Yamada et al., ACL-IJCNLP 2021)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2021.acl-short.102.pdf
- Data
- FrameNet