Improving Low Resource Named Entity Recognition using Cross-lingual Knowledge Transfer
Authors: Xiaocheng Feng, Xiachong Feng, Bing Qin, Zhangyin Feng, Ting Liu
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | 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 {xcfeng, xiachongfeng, bqin, zyfeng, tliu}@ir.hit.edu.cn |
| 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. |