Semantic Frame Identification with Distributed Word Representations

We present a novel technique for semantic frame identification using distributed representations of predicates and their syntactic context; this technique leverages automatic syntactic parses and a generic set of word embeddings. Given labeled data annotated with frame-semantic parses, we learn a model that projects the set of word representations for the syntactic context around a predicate to a low dimensional representation. The latter is used for semantic frame identification; with a standard argument identification method inspired by prior work, we achieve state-of-the-art results on FrameNet-style frame-semantic analysis. Additionally, we report strong results on PropBank-style semantic role labeling in comparison to prior work.

• Machine Intelligence

• Natural Language Processing