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- W2397419899 abstract "This paper describes a novel approach to find evidence for implicit semantic roles. Our data-driven models generalize over large amounts of explicit annotations only, in order to acquire information about implicit roles. We establish a generic background knowledge base of probablistic predicate-role co-occurrences in an unsupervised manner, and estimate thresholds which trigger the prediction of a missing role. Our approach outperforms the stateof-the-art in terms of recognition rate and offers a more flexible alternative to rulebased solutions which rely on costly, language and domain-specific lexica." @default.
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- W2397419899 date "2015-09-01" @default.
- W2397419899 modified "2023-09-26" @default.
- W2397419899 title "Towards the Unsupervised Acquisition of Implicit Semantic Roles" @default.
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