Abstract: In this research, we address the problem of stopword detection in Classical Chinese Poetry, an area that has not been explored previously. Stopword detection is crucial in text mining tasks, as identifying and removing stopwords is essential for improving the performance of various natural language processing models. Inspired by the TF-IDF method, we propose a novel approach that utilizes external knowledge to reconstruct the Term Weight matrix. Our key finding is that incorporating external knowledge significantly refines the granularity of the term weight, thereby improving the effectiveness of stopword detection. Based on these findings, we conclude that external knowledge can enhance the ability of text representation, especially for the short texts in Classical Chinese Poetry.

Lei Peng, Xiaodong Ma and Zheng Teng, “Detection of Stopwords in Classical Chinese Poetry” International Journal of Advanced Computer Science and Applications(IJACSA), 16(2), 2025. http://dx.doi.org/10.14569/IJACSA.2025.0160227

@article{Peng2025,
title = {Detection of Stopwords in Classical Chinese Poetry},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160227},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160227},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {2},
author = {Lei Peng and Xiaodong Ma and Zheng Teng}
}