In recent years, named entity recognition has always been a popular research
in the field of natural language processing, while traditional deep learning
methods require a large amount of labeled data for model training, which makes
them not suitable for areas where labeling resources are scarce. In addition,
the existing cross-domain knowledge transfer methods need to adjust the entity
labels for different fields, so as to increase the training cost. To solve
these problems, enlightened by a processing method of Chinese named entity
recognition, we propose to use domain knowledge to improve the performance of
named entity recognition in areas with low resources. The domain knowledge
mainly applied by us is domain dictionary and domain labeled data. We use
dictionary information for each word to strengthen its word embedding and
domain labeled data to reinforce the recognition effect. The proposed model
avoids large-scale data adjustments in different domains while handling named
entities recognition with low resources. Experiments demonstrate the
effectiveness of our method, which has achieved impressive results on the data
set in the field of scientific and technological equipment, and the F1 score
has been significantly improved compared with many other baseline methods.

本研究提出了一种基于领域知识的命名实体识别方法，使用领域词典和标注数据来提高低资源领域中的命名实体识别效果，避免大规模数据调整的同时，在科技设备领域数据集上实现了显著的 F1 分数提升。