@inproceedings{yamada-etal-2021-semantic,
title = "Semantic Frame Induction using Masked Word Embeddings and Two-Step Clustering",
author = "Yamada, Kosuke and
Sasano, Ryohei and
Takeda, Koichi",
editor = "Zong, Chengqing and
Xia, Fei and
Li, Wenjie and
Navigli, Roberto",
booktitle = "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 = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-short.102",
doi = "10.18653/v1/2021.acl-short.102",
pages = "811--816",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Semantic Frame Induction using Masked Word Embeddings and Two-Step Clustering
%A Yamada, Kosuke
%A Sasano, Ryohei
%A Takeda, Koichi
%Y Zong, Chengqing
%Y Xia, Fei
%Y Li, Wenjie
%Y Navigli, Roberto
%S 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)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F yamada-etal-2021-semantic
%X 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.
%R 10.18653/v1/2021.acl-short.102
%U https://aclanthology.org/2021.acl-short.102
%U https://doi.org/10.18653/v1/2021.acl-short.102
%P 811-816
Markdown (Informal)
[Semantic Frame Induction using Masked Word Embeddings and Two-Step Clustering](https://aclanthology.org/2021.acl-short.102) (Yamada et al., ACL-IJCNLP 2021)
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.