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Joint Learning Improves Semantic Role Labeling. In Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL'05), pages 589– ...
We propose a discriminative log-linear joint model for semantic role labeling, which incorpo- rates more global features and achieves superior performance in ...
Despite much recent progress on accurate semantic role labeling, previous work has largely used independent classifiers, possibly combined with separate ...
This system achieves an error reduction of 22% on all arguments and 32% on core arguments over a state- of-the art independent classifier for gold- standard ...
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2005. Joint learning improves semantic role labeling. In Proceedings of ACL-2005. 2On a 3.6GHz machine with 4GB of RAM.
In this article we consider the joint learning of semantic role labeling and dependency parsing. This means that our systems generate labels that carry ...
In the completion mode, the Joint model performs generally better than all the semi-supervised models. This phenomenon is likely because the Joint model is ...
Joint learning improves semantic role labeling. In ACL 2005. Nianwen Xue and Martha Palmer. 2004. Calibrating features for semantic role labeling. In ...
Results show that with the implicit encoding, the syntax information can further improve a state-of-the-art semantic role labeler. Keywords-Parsing, SRL, Joint ...