Character-Based Parsing with Convolutional Neural Network
Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence. Main Track. Pages 1054-1059.
Abstract
We describe a novel convolutional neural network architecture with k-max pooling layer that is able to successfully recover the structure of Chinese sentences. This network can capture active features for unseen segments of a sentence to measure how likely the segments are merged to be the constituents. Given an input sentence, after all the scores of possible segments are computed, an efficient dynamic programming parsing algorithm is used to find the globally optimal parse tree. A similar network is then applied to predict syntactic categories for every node in the parse tree. Our networks archived competitive performance to existing benchmark parsers on the CTB-5 dataset without any task-specific feature engineering.
@inproceedings{ijcai2015p153,
title = {Character-Based Parsing with Convolutional Neural Network},
author = {Zheng, Xiaoqing and Peng, Haoyuan and Chen, Yi and Zhang, Pengjing and Zhang, Wenqiang},
booktitle = {Proceedings of the Twenty-Fourth International Joint Conference on
Artificial Intelligence, {IJCAI-15}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Qiang Yang and Michael Wooldridge},
pages = {1054--1059},
year = {2015},
month = {7},
note = {Main Track},
url = {https://ijcai.org/proceedings/2015/153},
}
TY - CPAPER TI - Character-Based Parsing with Convolutional Neural Network AU - Zheng, Xiaoqing AU - Peng, Haoyuan AU - Chen, Yi AU - Zhang, Pengjing AU - Zhang, Wenqiang T2 - Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-15 PB - International Joint Conferences on Artificial Intelligence PY - 2015 SP - 1054 EP - 1059 UR - https://ijcai.org/proceedings/2015/153 ER -Copied
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