Multilingual Named Entity Recognition Using Pretrained Embeddings, Attention Mechanism and NCRF
Table 1: Evaluation metrics on development dataset
['label', 'precision', 'recall', 'f1-score']
On the prediction stage result, labels were received by voice classifier. After this, we transform word predictions to spans markup. For proposed neural network architecture the evaluation of the training stage was produced on development dataset. For development set, we obtained the following scores: language classification quality (f1-score): 0.998 and Multilingual Named Entity Recognition quality (f1-score): 0.70 for exact word level matching and 0.578 for exact full entities matching. Also we train model without language classification, which resulted in f1-score equal to 0.66 . This confirms the impact of language classification. Our model significantly outperforms the CRF baseline.