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..
Improving Low Resource Named Entity Recognition using Cross-lingual Knowledge Transfer
Authors: Xiaocheng Feng, Xiachong Feng, Bing Qin, Zhangyin Feng, Ting Liu
IJCAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on two low resource languages (Dutch and Spanish) demonstrate the effectiveness of these additional semantic representations (average 4.8% improvement). Moreover, on Chinese Onto Notes 4.0 dataset, our approach achieves an F-score of 83.07% with 2.91% absolute gain compared to the state-of-the-art systems. |
| Researcher Affiliation | Academia | Xiaocheng Feng, Xiachong Feng, Bing Qin , Zhangyin Feng, Ting Liu Harbin Institute of Technology, China EMAIL |
| Pseudocode | No | The paper includes architectural diagrams (Figure 2) and mathematical equations, but it does not provide any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is available at: https://github.com/scir-code/lrner. |
| Open Datasets | Yes | Table 2: # of sentences. Languages Dataset Train Dev Test Dutch Co NLL-2002 15520 2822 5077 Spanish Co NLL-2002 8323 1914 1517 Chinese Ontonotes 4.0 22761 3903 2730. Also mentions 'Co NLL: https://github.com/synalp/NER/tree/master/corpus/' and 'Ontonotes: https://catalog.ldc.upenn.edu/ldc2011t03'. |
| Dataset Splits | Yes | Table 2: # of sentences. Languages Dataset Train Dev Test Dutch Co NLL-2002 15520 2822 5077 Spanish Co NLL-2002 8323 1914 1517 Chinese Ontonotes 4.0 22761 3903 2730 |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments (e.g., GPU models, CPU types, or memory specifications). |
| Software Dependencies | No | The paper mentions 'LSTM-CRF model is originally introduced by [Huang et al., 2015]' and refers to 'LSTM-CRF framework [Lample et al., 2016]' and states 'Our code is available at: https://github.com/scir-code/lrner.' but it does not list specific software dependencies with their version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | We randomize other parameters with uniform distribution U(0.01, 0.01), and set the learning rate as 0.01. |