Verb Sense Clustering using Contextualized Word Representations for Semantic Frame Induction
Verb Sense Clustering using Contextualized Word Representations for Semantic Frame Induction
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DOI：
10.18653/v1/2021.findings-acl.381
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发表时间：
2021-05
期刊：
影响因子：
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通讯作者：
Kosuke Yamada;Ryohei Sasano;Koichi Takeda
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文献类型：
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作者：
Kosuke Yamada;Ryohei Sasano;Koichi Takeda
上下文化的词表示已被证明对于各种自然语言处理任务是有用的。然而，目前还不清楚这些表示在多大程度上可以覆盖手工编码的语义信息，如语义框架，它指定了与谓词相关的参数的语义角色。在本文中，我们专注于动词，唤起不同的框架取决于上下文，我们调查如何以及语境化的词表示可以识别的框架，同一个动词唤起的差异。我们还探讨了哪些类型的表示是适合语义框架归纳。在我们的实验中，我们比较了七个不同的语境化的词表示的两个英语框架语义资源，框架网络和PropBank。我们证明了几个语境化的词表示，特别是BERT及其变体，是相当翔实的语义框架归纳。此外，我们还考察了动词的语境化表征在多大程度上可以估计动词所能唤起的框架数量。
Contextualized word representations have proven useful for various natural language processing tasks. However, it remains unclear to what extent these representations can cover hand-coded semantic information such as semantic frames, which specify the semantic role of the arguments associated with a predicate. In this paper, we focus on verbs that evoke different frames depending on the context, and we investigate how well contextualized word representations can recognize the difference of frames that the same verb evokes. We also explore which types of representation are suitable for semantic frame induction. In our experiments, we compare seven different contextualized word representations for two English frame-semantic resources, FrameNet and PropBank. We demonstrate that several contextualized word representations, especially BERT and its variants, are considerably informative for semantic frame induction. Furthermore, we examine the extent to which the contextualized representation of a verb can estimate the number of frames that the verb can evoke.