关键词: 中文命名实体识别, 深度学习, 景点识别, 卷积神经网络（CNN）, 双向长短记忆网络（BiLSTM）, 条件随机场（CRF）

Abstract:

Named entity recognition is one of important stages in natural language processing. In recent years, the model based on deep learning has achieved remarkable results on open domain named entity recognition. However in tourism domain, Chinese attractions entity recognition methods often rely on feature engineering. A neural network model based on CNN-BiLSTM-CRF is proposed. The model doesn’t add any artificial features, extracts and expresses the local information features of the text by neural network, and learns and utilizes the context information of the text to recognition the scenic spots entities. The experimental results show that the method can effectively identify Chinese tourist attractions entities, and the [F1] value is 93.9% in the experiment.

Key words: Chinese named entity recognition, deep learning, scenic spot recognition, Convolutional Neural Network（CNN）, Bidirectional Long Short Term Memory（BiLSTM）, Conditional Random Field（CRF）