# Candidate Term Boundary Conflict Reduction Method for Chinese Geological Text Segmentation
## 4 Experiments and Discussion
### More Comparisons with SOTA Model  
In this paper, more SOTA models including GPT-3 and WMSEG are considered for comparison, except for representative tokenizers.  
GPT-3 is a powerful third-generation language model developed by OpenAI and has 175 billion parameters, which is a huge and complex model. It is considered to be one of the most advanced language models available, and tasks are implemented with very simple instruction. In our experiments, we used the Davinci language model of GPT-3 and found that there is great uncertainty between the results for the same instruction. There are also restrictions on the number and length of long-text word segmentation. For the uncertainty of a short geological text result, experiments show that six types of different segmentation results can be generated in twenty random segmentations, among which three results are wrong, and the accuracy rate is about 85%. The average accuracy of word segmentation for twenty repeated experiments is shown in Table 6 below.  
WMSEG is a neural framework, which uses memory networks to incorporate wordhood information with popular encoder-decoder combinations for CWS [6]. It belongs to the integrated approach and achieves state-of-the-art performance on all general datasets. However, its BERT-based pre-trained model underperforms in our experiments for geological text and has limitations on the data format and text length. The specific experimental accuracy is shown in Table 6 below.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Description** & **Text** \\ \hline \multirow{6}{*}{Result in Pinyin} & /[PERSON]/[PERSON]/[PERSON]/[PERSON]/./[PERSON]/[PERSON]/[PERSON]/[PERSON]/[PERSON]/[PERSON]/[PERSON]//[PERSON]/[PERSON]/[PERSON] \\  & [PERSON]/[PERSON]/[PERSON]//./[PERSON]/[PERSON]/[PERSON]/[PERSON] \\  & [PERSON]/[PERSON]/[PERSON]/[PERSON]/[PERSON]/[PERSON]/[PERSON] \\  & [PERSON]//./[PERSON]/[PERSON]/[PERSON]/[PERSON]/[PERSON] \\  & [PERSON]/[PERSON]/[PERSON]/[PERSON]/[PERSON]/[PERSON]/[PERSON]/. \\  & /These/special/designs//.for/the patients/here/is \\  & intimate/care//./As/part/of the/University \\  & of/Oslo//./Sonnos/Hospital/is responsible \\ Result in English & for/several/important/research/on the rehabilitation \\  & of/persons with disabilities/and is/the main/hospital/in \\  & Norway/ for research/on surgical \\  & medicine/and/rehabilitation/. \\ \hline \hline \end{tabular}
\end{table}
Table 4: Result display of the HCRF algorithm for general text segmentation.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Data** & **Tools** & **Accuracy** & **Precision** & **Recall** & **F1-value** \\ \hline \multirow{6}{*}{MSR} & HFCR & 79.37\% & 86.63\% & 87.51\% & 87.07\% \\  & Baidu LAC & 86.46\% & 90.41\% & 94.35\% & 92.38\% \\  & Pkuseg & 87.40\% & 94.81\% & 90.54\% & 92.63\% \\  & Hanlp & 85.53\% & 90.99\% & 92.21\% & 91.60\% \\  & Jieba & 78.63\% & 87.85\% & 84.50\% & 86.15\% \\ \hline \multirow{6}{*}{PKU} & HFCR & 78.93\% & 83.20\% & 91.86\% & 87.32\% \\  & Baidu LAC & 82.51\% & 84.00\% & 97.35\% & 90.18\% \\ \cline{1-1}  & Pkuseg & 90.25\% & 92.41\% & 97.18\% & 94.73\% \\ \cline{1-1}  & Hanlp & 86.43\% & 88.20\% & 97.32\% & 92.54\% \\ \cline{1-1}  & Jieba & 77.17\% & 83.22\% & 88.20\% & 85.64\% \\ \hline \hline \end{tabular}  
* The underlined part indicates our performance and the optimal segmentation performance.  
\end{table}
Table 5: Comparison of accuracy between HFCR algorithm and other tokenizers (general text).