Title: Research on philosophy sentence segmentation algorithm based on BERT pre-training and deep learning network

Authors: Dan Cheng

Addresses: College of Marxism, Northwest Normal University, Lanzhou, 730070, China; School of Marxism, Xi'an Technological University, Xian, 710021, China

Abstract: To improve the accuracy of sentence breaking in ancient philosophy texts, based on BERT pre-training and deep learning network, an ancient philosophy sentence segmentation algorithm with high accuracy is proposed. Based on Lattice model, BERT model is used to encode ancient philosophical texts into word vectors, and flat-lattice transformer (FLT) position coding method is adopted to determine the interaction relationship between vectors. Then, sentence segmentation situation of ancient philosophical texts is predicted. Finally, sentence segmentation accuracy of ancient philosophical texts is improved. The simulation results show that compared with Lattice-LAN, BERT-Bi-LSTM-CRF and Bi-LSTM-CRF models, the proposed algorithm has obvious advantages in the accuracy of ancient philosophy sentence segmentation. Moreover, accuracy, recall, and F-value of the proposed algorithm are 82.82%, 70.71%, and 76.29%, respectively, which improves the sentence segmentation accuracy of ancient philosophical texts and has certain reference value.

Keywords: philosophy sentence segmentation; BERT pre-training; deep learning; LSTM model; FLT position coding.

DOI: 10.1504/IJBIC.2026.155381

International Journal of Bio-Inspired Computation, 2026 Vol.28 No.1, pp.1 - 11

Received: 05 Feb 2024
Accepted: 19 Oct 2024
Published online: 31 Jul 2026 *

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