基于大语言模型的社交媒体中文命名实体识别方法

Jiang Da-Rui , He Min-Wei, XU sheng-chao

摘要:

社交媒体中的中文文本因其类别模糊等特性，导致文本特征提取困难，进而影响了命名实体识别的准确性。为此提出基于大语言模型的社交媒体中文命名实体识别方法。该方法从社交媒体平台采集大量中文文本数据，并对其进行语义增强处理，随后设计文本信息编码器，对增强后的文本进行相对位置编码。在此基础上，利用大语言模型深度挖掘文本信息特征，构建特征向量表示。同时，引入注意力机制，分析文本上下文的语义特征。通过标签解码过程，输出最优的文本序列，从而构建中文命名实体识别模型。在损失函数的指导下，对模型参数进行优化，实现对命名实体的精确识别。实验结果表明，该方法在实际应用中表现出0.03%的误识率，具备较高的识别精度和良好的应用效果。

Abstract:

Chinese text in social media is difficult to extract text features because of its fuzzy category, which affects the accuracy of named entity recognition. Therefore, a large language model based Chinese named entity recognition method for social media is proposed. This method collects a large number of Chinese text data from social media platforms, enhances the semantic processing of the data, and then designs a text encoder to encode the relative position of the enhanced text. On this basis, a large language model is used to deeply explore the text information features and construct the feature vector representation. At the same time, the attention mechanism is introduced to analyze the semantic features of text context. Through the label decoding process, the optimal text sequence is output, and the Chinese named entity recognition model is constructed. Under the guidance of the loss function, the model parameters are optimized to realize the accurate identification of named entities. The experimental results show that the error rate of this method is 0.03% in practical application, which has high recognition accuracy and good application effect.