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Second-Order Semantic Dependency Parsing with End-to-End Neural Networks
2019
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Semantic dependency parsing aims to identify semantic relationships between words in a sentence that form a graph. In this paper, we propose a second-order semantic dependency parser, which takes into consideration not only individual dependency edges but also interactions between pairs of edges. We show that second-order parsing can be approximated using mean field (MF) variational inference or loopy belief propagation (LBP). We can unfold both algorithms as recurrent layers of a neural
doi:10.18653/v1/p19-1454
dblp:conf/acl/WangHT19
fatcat:k3vvow4jtzexzc3wgwissy4jvi