Character-Based Parsing with Convolutional Neural Network
Authors: Xiaoqing Zheng, Haoyuan Peng, Yi Chen, Pengjing Zhang, Wenqiang Zhang
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that the KMCNN can successfully recover the structure of Chinese sentences. We conducted two sets of experiments. |
| Researcher Affiliation | Academia | Xiaoqing Zheng, Haoyuan Peng, Yi Chen, Pengjing Zhang, Wenqiang Zhang School of Computer Science, Fudan University, Shanghai, China Shanghai Key Laboratory of Intelligent Information Processing {zhengxq, penghy11, yichen11, pengjingzhang13, wqzhang}@fudan.edu.cn |
| Pseudocode | No | The paper describes the dynamic programming decoding algorithm in text and refers to a figure, but it does not provide explicit pseudocode or algorithm blocks. |
| Open Source Code | No | The paper mentions using a third-party tool 'Word2Vec' and provides a link for it, but it does not state that the code for their own described methodology (KMCNN) is open-source or provide a link to it. |
| Open Datasets | Yes | We compared the performance of the KMCNN with the existing stateof-the-art systems on the Penn Chinese Treebank 5 (CTB-5) using the standard split of data1. We transformed the parse trees of the CTB-5 into their binary forms for training by the method described in Section 2. |
| Dataset Splits | Yes | Sections 1-270, 400-931, 1001-1151 are used for training, sections 301-325 for development, and sections 271-300 for testing. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running its experiments. |
| Software Dependencies | No | The paper mentions the 'Word2Vec tool' but does not specify any software dependencies with version numbers for its own implementation or experimental setup. |
| Experiment Setup | Yes | Table 1: Hyper-parameters of the network. Hyper-parameter Value Window sizes of two convolutional layers 5/3 Values k in two k-max pooling layers 5/3 Number of convolutional units 200 Number of hidden units 300 Character feature dimension 50 Learning rate 0.02 Penalization term κ 0.05 Regularization parameter λ 0.0001 |