Multi-task learning for Chinese clinical named entity recognition with external knowledge
Background Named entity recognition (NER) on Chinese electronic medical/healthcare
records has attracted significantly attentions as it can be applied to building applications to
understand these records. Most previous methods have been purely data-driven, requiring
high-quality and large-scale labeled medical data. However, labeled data is expensive to
obtain, and these data-driven methods are difficult to handle rare and unseen entities.
Methods To tackle these problems, this study presents a novel multi-task deep neural …
records has attracted significantly attentions as it can be applied to building applications to
understand these records. Most previous methods have been purely data-driven, requiring
high-quality and large-scale labeled medical data. However, labeled data is expensive to
obtain, and these data-driven methods are difficult to handle rare and unseen entities.
Methods To tackle these problems, this study presents a novel multi-task deep neural …
Background
Named entity recognition (NER) on Chinese electronic medical/healthcare records has attracted significantly attentions as it can be applied to building applications to understand these records. Most previous methods have been purely data-driven, requiring high-quality and large-scale labeled medical data. However, labeled data is expensive to obtain, and these data-driven methods are difficult to handle rare and unseen entities.
Methods
To tackle these problems, this study presents a novel multi-task deep neural network model for Chinese NER in the medical domain. We incorporate dictionary features into neural networks, and a general secondary named entity segmentation is used as auxiliary task to improve the performance of the primary task of named entity recognition.
Results
In order to evaluate the proposed method, we compare it with other currently popular methods, on three benchmark datasets. Two of the datasets are publicly available, and the other one is constructed by us. Experimental results show that the proposed model achieves 91.07% average f-measure on the two public datasets and 87.05% f-measure on private dataset.
Conclusions
The comparison results of different models demonstrated the effectiveness of our model. The proposed model outperformed traditional statistical models.
Springer
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