BRFP: An Efficient and Universal Sentence Embedding Learning Model Method Supporting Fused Syntax Combined with Graph Embedding Representation Algorithm

Due to the rapidly growing volume of data on the Internet, the methods of efficiently and accurately processing massive text information have been the focus of research. In natural language processing theory, sentence embedding representation is an important method. This paper proposes a new sentence embedding learning model called BRFP (Factorization Process with Bidirectional Restraints) that fuses syntactic information, uses matrix decomposition to learn syntactic information, and fuses and calculates with word vectors to obtain the embedded representation of sentences. In the experimental chapter, text similarity experiments are conducted to verify the rationality and effectiveness of the model and analyzed experimental results on Chinese and English texts with the current mainstream learning methods, and potential improvement directions are summarized. The experimental results on Chinese and English datasets, including STS, AFQMC, and LCQMC, show that the model proposed in this paper outperforms the CNN method in terms of accuracy and F1 value by 7.6% and 4.8. The comparison experiment with the word vector weighted model shows that when the sentence length is longer, or the corresponding syntactic structure is complex, the model’s advantages in this paper are more prominent than TF-IDF and SIF methods. Compared with the TF-IDF method, the effect improved by 14.4%. Compared with the SIF method, it has a maximum advantage of 7.9%, and the overall improvement in each comparative experimental task is between 4 and 6 percentage points. In the neural network model comparison experiment, the model in this paper compared the CNN, RNN, LSTM, ST, QT, and InferSent models, and the effect significantly improved on the 14’OnWN, 14’Tweet-news, and 15’Ans.-forum datasets. For example, in the 14’OnWN dataset, the BRFP method has a 10.9% improvement over the ST method. The 14’Tweet-news dataset has a 22.9% advantage over the LSTM method, and the 15’Ans.-forum dataset has a 24.07% improvement over the RNN method. The article also demonstrates the generality of the model, proving that the model proposed in this paper is also a universal learning framework.