Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Chinese NER with Height-Limited Constituent Parsing
Authors: Rui Wang, Xin Xin, Wei Chang, Kun Ming, Biao Li, Xin Fan7160-7167
AAAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on the Onto Notes 4.0 dataset have demonstrated that the proposed model outperforms the state-of-the-art method by 2.79 points in the F1-measure. |
| Researcher Affiliation | Collaboration | Rui Wang,1 Xin Xin,1 Wei Chang,1 Kun Ming,1 Biao Li,2 Xin Fan2 1BJ ER Center of HVLIP&CC, School of Comp. Sci. & Tech., Beijing Institute of Technology, Beijing, China 2Tencent, Beijing, China EMAIL, EMAIL |
| Pseudocode | No | The paper describes algorithms (dynamic programming, pruning) but does not include a clearly labeled pseudocode or algorithm block. |
| Open Source Code | No | The paper does not provide an unambiguous statement or a link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | The experiments are conducted on the dataset of Onto Notes 4.0 (Weischedel et al. 2011). |
| Dataset Splits | Yes | The dataset contains 15,724 sentences in the training set, 4,301 sentences in the development set, and 4,346 sentences in the testing set, with more than 490,000 characters in total. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments, such as GPU or CPU models. |
| Software Dependencies | No | The paper mentions using systems like ZPar and Lattice LSTM, but does not provide specific version numbers for any software dependencies or libraries. |
| Experiment Setup | Yes | Table 1: Hyper-parameters. Some of them are employed from the work of (Zhang and Yang 2018), and others are set according to parameter analysis. |