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.

本研究提出了使用掩码词嵌入和两步聚类的语义框架归纳方法，旨在克服使用基于聚类的方法与上下文嵌入时，对诱发框架动词的表面信息过于依赖和将同一动词的实例分成过多框架族群的问题，实验结果表明，该方法对于避免使用表面信息和改进相应的框架族群数量是有效的。

利用掩码词嵌入和两步聚类进行语义框架归纳