  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.
