A re-ranking model for dependency parser with recursive Convolutional neural network

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Abstract

In this work, we address the problem to model all the nodes (words or phrases) in a dependency tree with the dense representations. We propose a recursive convolutional neural network (RCNN) architecture to capture syntactic and compositional-semantic representations of phrases and words in a dependency tree. Different with the original recursive neural network, we introduce the convolution and pooling layers, which can model a variety of compositions by the feature maps and choose the most informative compositions by the pooling layers. Based on RCNN, we use a discriminative model to re-rank a k-best list of candidate dependency parsing trees. The experiments show that RCNN is very effective to improve the state-of-The-Art dependency parsing on both English and Chinese datasets.

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Zhu, C., Qiu, X., Chen, X., & Huang, X. (2015). A re-ranking model for dependency parser with recursive Convolutional neural network. 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. 1, pp. 1159–1168). Association for Computational Linguistics (ACL). https://doi.org/10.3115/v1/p15-1112

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