Frame-semantic role labeling with heterogeneous annotations

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Abstract

We consider the task of identifying and la-beling the semantic arguments of a predi-cate that evokes a FrameNet frame. This task is challenging because there are only a few thousand fully annotated sentences for supervised training. Our approach aug-ments an existing model with features de-rived from FrameNet and PropBank and with partially annotated exemplars from FrameNet. We observe a 4% absolute in-crease in F1 versus the original model.

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Kshirsagar, M., Thomson, S., Schneider, N., Carbonell, J., Smith, N. A., & Dyer, C. (2015). Frame-semantic role labeling with heterogeneous annotations. In ACL-IJCNLP 2015 - 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing, Proceedings of the Conference (Vol. 2, pp. 218–224). Association for Computational Linguistics (ACL). https://doi.org/10.3115/v1/p15-2036

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