Semantic Frame Induction using Masked Word Embeddings and Two-Step Clustering「マスクされた単語埋め込みと2段階クラスタリングを用いた動詞の意味フレーム推定」の解説
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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 dataset from the English FrameNet, 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.
Semantic Frame Induction of Verbs using Contexutualized Word Representations
- 山田康輔
The Berkeley FrameNet Project
- C F Baker
- C J Fillmore
- J B Lowe
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
- J Devlin
- M.-W Chang
- K Lee
- K Toutanova