Chinese Named Entity Recognition for Clothing Knowledge Graph Construction

Abstract

Abstract Clothing knowledge graph is a kind of vertical domain knowledge base constructed for the description of clothing knowledge in the field of textile and apparel. In this paper, based on the limitations of the clothing knowledge graph in the effect of entity extraction, the deep learning model and the statistical model are combined. A Chinese named entity recognition method based on CNN-BiLSTM-CRF is proposed. Firstly, the convolutional neural network(CNN) is used to extract the text features, and the character-level vectors with morphological features of the words are trained. Then the bi-directional long short term memory networks(LSTM) is used to learn the context features, and the vector representation of the context of each word is output. Finally, the conditional random fields(CRF) model is used for self-learning. Get the best tag sequence for the sentence. The method can automatically recognize the text, and does not rely on the artificial feature to obtain the semantic category information. Finally, the experimental data and evaluation methods are introduced. The experimental results show that the Chinese named entity recognition method based on CNN-BiLSTM-CRF is superior to other models in all indicators, indicating the effectiveness of the method.