Entity Relation Extraction of Traditional Chinese Medicine Influenza Based on Bi-GRU+GBDT

Abstract

In this paper, an algorithm based on Bi-directional Gated Recurrent Unit (Bi-GRU) and Gradient Boosting Decision Tree (GBDT) is proposed to extract the entity relationship of Traditional Chinese Medicine influenza. Firstly, the word vector is used as the input data set and the word vector is constructed by the word embedding model Word2Vec tool. Then the sentence feature is extracted by Bi-GRU and the attention mechanism is integrated to improve the accuracy of feature extraction. Finally, the feature vector is input into the GBDT algorithm for classification training and prediction to complete the Traditional Chinese Medicine influenza entity relationship extraction. In this paper, a variety of different entity relation extraction algorithms are compared with this algorithm to verify the effectiveness of the algorithm. This algorithm improves the stability of the model and effectively solves the problem of insufficient generalization ability of the model. Therefore, when studying the relationship extraction of Traditional Chinese Medicine texts, we can give priority to using Bi-GRU+GBDT model. Also through the experiment to adjust the model parameters and comparison to get the optimal parameters of Traditional Chinese Medicine influenza relationship extraction experiment.