Frequent tags taskpaper6/28/2023 ![]() ![]() Some of the material presented in this article has appeared in previously published conference papers: Das et al. Herein, we also present results on newly released data with FrameNet 1.5, the latest edition of the lexicon. Our open-source parser, named SEMAFOR (Semantic Analyzer of Frame Representations) 1 achieves the best published results to date on the SemEval 2007 frame-semantic structure extraction task (Baker, Ellsworth, and Erk 2007). Some novel aspects of our approach include a latent-variable model ( Section 5.2) and a semi-supervised extension of the predicate lexicon ( Section 5.5) to facilitate disambiguation of words not in the FrameNet lexicon a unified model for finding and labeling arguments ( Section 6) that diverges from prior work in semantic role labeling and an exact dual decomposition algorithm ( Section 7) that collectively predicts all the arguments of a frame together, thereby incorporating linguistic constraints in a principled fashion. Experiments demonstrating favorable performance to the previous state of the art on SemEval 2007 and FrameNet data sets are described in each section. We decompose this task into three subproblems: target identification ( Section 4), in which frame-evoking predicates are marked in the sentence frame identification ( Section 5), in which the evoked frame is selected for each predicate and argument identification ( Section 6), in which arguments to each frame are identified and labeled with a role from that frame. We have released our frame-semantic parser as open-source software.Ĭarefully constructed lexical resources and annotated data sets from FrameNet, detailed in Section 3, form the basis of the frame structure prediction task. Additionally, we present experiments on the much larger FrameNet 1.5 data set. 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. Both components are feature-based and discriminatively trained on a small set of annotated frame-semantic parses. 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. The second stage finds the target's locally expressed semantic arguments. This model uses latent variables and semi-supervised learning to improve frame disambiguation for targets unseen at training time. Given a target in context, the first stage disambiguates it to a semantic frame. 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. Frame semantics is a linguistic theory that has been instantiated for English in the FrameNet lexicon.
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