Frame semantics (Fillmore 1982) is a linguistic theory that has been instantiated for English in
the FrameNet lexicon (Fillmore, Johnson, and Petruck 2003). We solve the problem of frame-
semantic parsing using a two-stage statistical model that takes lexical targets (i.e., content
words and phrases) in their sentential context and predicts frame-semantic structures. Given a
target in context, the first stage disambiguates it to a semantic frame. This model employs latent
variables and semi-supervised learning to improve frame disambiguation for targets unseen at
training time. The second stage finds the target’s locally expressed semantic arguments. A fast
exact dual decomposition algorithm collectively predicts all the arguments of a frame at once
in order to respect declaratively stated linguistic constraints at inference time, resulting in
qualitatively better structures than naïve local predictors. Both components are feature-based
and discriminatively trained on a small set of annotated frame-semantic parses. On a benchmark
dataset, the approach, along with a heuristic identifier of frame-evoking targets, outperforms
prior state of the art by significant margins. Additionally, we present experiments on a much
larger recent dataset and have released our accurate frame-semantic parser as open-source
software.

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