@article{Shi2023, 
author = {Yunmei Shi and Yuanhua Li and Ning Li},
title = {Sentence coherence evaluation based on neural network and textual features for official documents},
year = {2023},
journal = {Electronic Research Archive},
volume = {31},
number = {6},
pages = {3609-3624},
keywords = {sentence coherence, XL-Net, repetitive words, feature fusion},
url = {https://www.sciopen.com/article/10.3934/era.2023183},
doi = {10.3934/era.2023183},
abstract = {Sentence coherence is an essential foundation for discourse coherence in natural language processing, as it plays a vital role in enhancing language expression, text readability, and improving the quality of written documents. With the development of e-government, automatic generation of official documents can significantly reduce the writing burden of government agencies. To ensure that the automatically generated official documents are coherent, we propose a sentence coherence evaluation model integrating repetitive words features, which introduces repetitive words features with neural network-based approach for the first time. Experiments were conducted on official documents dataset and THUCNews public dataset, our method has achieved an averaged 3.8% improvement in accuracy indicator compared to past research, reaching a 96.2% accuracy rate. This result is significantly better than the previous best method, proving the superiority of our approach in solving this problem.}
}