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Frame-Semantic Parsing
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In: MIT Press (2012)
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Abstract:
Frame semantics is a linguistic theory that has been instantiated for English in the FrameNet lexicon. 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 contexts and predicts frame-semantic structures. Given a target in context, the first stage disambiguates it to a semantic frame. This model uses 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. At inference time, a fast exact dual decomposition algorithm collectively predicts all the arguments of a frame at once in order to respect declaratively stated linguistic constraints, 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 the SemEval 2007 benchmark data set, the approach, along with a heuristic identifier of frame-evoking targets, outperforms the prior state of the art by significant margins. Additionally, we present experiments on the much larger FrameNet 1.5 data set. We have released our frame-semantic parser as open-source software. ; United States. Defense Advanced Research Projects Agency (DARPA grant NBCH-1080004) ; National Science Foundation (U.S.) (NSF grant IIS-0836431) ; National Science Foundation (U.S.) (NSF grant IIS-0915187) ; Qatar National Research Fund (NPRP 08-485-1-083)
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URL: http://hdl.handle.net/1721.1/88418
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