Semantic Frame Induction using Masked Word Embeddings and Two-Step Clustering

Kosuke Yamada, Ryohei Sasano, Koichi Takeda


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
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
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.acl-short.102.pdf