在教育领域中,命名实体识别在机器自动提问和智能问答等相关任务中都有应用。传统的中文命名实体识别模型需要改变网络结构来融入字和词信息,增加了网络结构的复杂度。另一方面,教育领域中的数据对实体边界的识别要十分精确,传统方法未能融入位置信息,对实体边界的识别能力较差。针对以上的问题,该文使用改进的向量表示层,在向量表示层中融合字、词和位置信息,能够更好地界定实体边界和提高实体识别的准确率,使用BiGRU和CRF分别作为模型的序列建模层和标注层进行中文命名实体识别。该文在Resume数据集和教育数据集(Edu)上进行了实验,F1值分别为95.20%和95.08%。实验结果表明,该文方法对比基线模型提升了模型的训练速度和实体识别的准确性。
Abstract
In the field of education, named entity recognition is widely used in Automatic machine questioning and Intelligent question answering. The traditional Chinese named entity recognition model needs to change the network structure to incorporate character and word information, which increases the complexity of the network structure. On the other hand, the data in the education field must be very accurate in the identification of entity boundaries. Traditional methods cannot incorporate location information, and the ability to identify entity boundaries is poor. In response to the above problems, this article uses an improved vector representation layer to integrate words, character, and location information in the vector representation layer, which can better define entity boundaries and improve the accuracy of entity recognition. BiGRU and CRF are used as models respectively. The sequence modeling layer and the annotation layer perform Chinese named entity recognition. This article conducted experiments on the Resume data set and the education data set (Edu), and the F1 values were 95.20% and 95.08%, respectively. The experimental results show that the method proposed in this paper improves the training speed of the model and the accuracy of entity recognition compared with the baseline model.
关键词
中文命名实体识别 /
BiGRU-CRF /
简单向量表示层(SVR)
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Key words
Chinese NER /
BiGRU-CRF /
simple vectors representation layers (SVR)
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参考文献
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基金
国家自然科学基金联合基金(U1811261);中央高校基本科研业务费专项资金(N2116019)
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