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

• URL: http://arxiv.org/abs/2105.13466v1
• Date: Thu, 27 May 2021 22:00:33 GMT
• Title: Semantic Frame Induction using Masked Word Embeddings and Two-Step Clustering
• Authors: Kosuke Yamada, Ryohei Sasano, Koichi Takeda
• Abstract summary: We propose a semantic frame induction method using masked word embeddings and two-step clustering. We demonstrate that using the masked word embeddings is effective for avoiding too much reliance on the surface information of frame-evoking verbs.
• Score: 9.93359829907774
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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