Named entity recognition (NER) is an information extraction technique that aims to locate and classify named entities within a document into predefined categories. However, it is still a difficult task because named entities (NEs) have multiple forms and they are context dependent. We propose the combination of contextual features from XLNet and global features from Graph Convolution Network (GCN) to enhance NER performance. Experiments over a widely-useddataset, CoNLL 2003, show the benefits of our strategy, with results competitive with the state of the art.
All the necessary library are noted in requirements.txt, please run the following command to install:
pip install -r requirements.txtRun the following command to train the model:
btslen.shThe train.py contains XLNet model whereas the combined_train.py includes the combination of XLNet and GCN, which is also our finalized model. Feel free to change the hyperparameters inside btslen.sh for further experiments and model tuning.
| Embeddings | F1-score |
|---|---|
| Global features | 88.63 |
| Contextual features | 93.28 |
| Global + contextual features | 93.82 |
| Entity types | Precision | Recall | F1-score |
|---|---|---|---|
| LOC | 94.15 | 93.53 | 93.83 |
| MISC | 81.33 | 81.89 | 81.62 |
| ORG | 88.97 | 92.29 | 90.60 |
| PER | 96.67 | 97.09 | 96.88 |
Hanh, Tran Thi Hong, et al. "Named Entity Recognition Architecture Combining Contextual and Global Features." International Conference on Asian Digital Libraries. Springer, Cham, 2021.
- Hồng Hạnh
- Prof. dr. Nicolas SIDERE
- Prof. dr. Antoine DOUCET
- Prof. dr. Jose MORENO
- Asst. prof. dr. Senja POLLAK.