Journal
COMPUTATIONAL LINGUISTICS
Volume 40, Issue 1, Pages 9-56Publisher
MIT PRESS
DOI: 10.1162/COLI_a_00163
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Funding
- DARPA [NBCH-1080004]
- NSF [IIS-0836431, IIS-0915187]
- Qatar National Research Foundation [NPRP 08-485-1-083]
- Pittsburgh Supercomputing Center [TG-DBS110003]
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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 naive 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.
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