While FrameNet is widely regarded as a rich resource of semantics in natural language processing, a major criticism concerns its lack of coverage and the relative paucity of its labeled data compared to other commonly used lexical resources such as PropBank and VerbNet. This paper reports on a pilot study to address these gaps. We propose a data augmentation approach, which uses existing frame-specific annotation to automatically annotate other lexical units of the same frame which are unannotated. Our rule-based approach defines the notion of a sister lexical unit and generates frame-specific augmented data for training. We present experiments on frame-semantic role labeling which demonstrate the importance of this data augmentation: we obtain a large improvement to prior results on frame identification and argument identification for FrameNet, utilizing both full-text and lexicographic annotations under FrameNet. Our findings on data augmentation highlight the value of automatic resource creation for improved models in frame-semantic parsing.

本文旨在通过提出数据增量方法来解决FrameNet在语义方面的短板，该方法通过利用现有的特定于框架的标注来自动标注未标注的同一框架的其他词汇单位。我们的基于规则的方法定义了姐妹词汇单位的概念，并为训练生成了特定于框架的增强数据。在FrameNet下的全文和词典注释下，我们展示了框架语义角色标注的实验结果，并证明了数据增加对于提高框架语义分析中的先前结果标识和参数识别至关重要，突显了自动资源创建对于改善模型的价值。

Sister Help：用于帧-语义角色标注的数据增强方法