基于ERNIE和BiLSTM的中文名词隐喻识别

Recognizing Chinese noun metaphors with ERNIE and BiLSTM

隐喻普遍存在于自然语言中,精确的识别隐喻可以促进自然语言处理具体任务中语义的理解.为了提高中文名词隐喻识别效果,提出一种基于知识增强的语义表示(ERNIE)和双向长短期记忆网络(BiLSTM)的分类模型ERNIE_BiLSTM.该模型使用ERNIE进行编码,获取上下文相关的语义表示;并利用双向LSTM再次训练词向量,捕获文本长距离的语义关系.实验结果表明,ERNIE_BiLSTM模型在中文名词隐喻识别数据集上表现良好,准确率达到90.34%.

Metaphors are ubiquitous in natural language.Accurate identification of metaphors can promote the understanding of semantics in specific tasks of natural language processing.In order to improve the recognition effect of Chinese noun metaphors,a classification model ERNIE_BiLSTM based on Enhanced Representation through Knowledge Integration(ERNIE)and Bidirectional Long Short-Term Memory(BiLSTM)was proposed.The model used ERNIE for encoding to obtain context-sensitive semantic representations and used BiLSTM for training the word vector to capture the long-distance semantic relationship of the text.Experimental results showed that the ERNIE_BiLSTM model performed well on the Chinese noun metaphor recognition data set,with an accuracy rate of 90.34%.