## 3 Results  
### Characteristics of Lexical Features within Domain Concepts  
Table 3 lists descriptive statistics of the lexical features in our data set. Domain concepts' CL and SN values, respectively, ranged from 1 to 15 and from 1 to 12.48, indicating large differences in their word forms. Domain concepts' FOU values were transformed into log  
Figure 2: An example of SHAP values in our dataset.  
frequencies ranging from 0 to 4.51. The mean values of the three principal components in SI, POS, and CPA were roughly equal to 0: all these values were normalized to a standard distribution during principal component analysis. The normality test results collected from the Kolmogorov-Smirnov (K-S) test also suggested that the values of lexical features did not conform to a normal distribution (i.e., all \(p\) values were less than 0.05).  
Feature correlations are a key aspect of data modeling alongside familiarity prediction in our case. Highly correlated features can obscure each feature's standalone importance. Multicollinearity tests are usually performed for clarity. Figure 3 displays the results of a  
Figure 3: Analysis of lexical feature correlations and clustering.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Feature**} & \multirow{2}{*}{**Mean**} & \multirow{2}{*}{**STD**} & \multirow{2}{*}{**Min**} & \multirow{2}{*}{**Max**} & \multicolumn{2}{c}{**Percentile**} & \multicolumn{2}{c}{**K-S Test**} \\ \cline{5-10}  & & & & & **25** & **50** & **75** & **K-S Statistic** & \(p\)**Value** \\ \hline CL & 5.11 & 1.71 & 1.00 & 15.00 & 4.00 & 5.00 & 6.00 & 368.52 & 0.00 \\ \hline SN & 7.09 & 1.74 & 1.00 & 12.48 & 5.98 & 7.10 & 8.20 & 27.78 & 0.00 \\ \hline FOU & 2.51 & 1.17 & 0.00 & 4.51 & 1.53 & 2.76 & 3.50 & 795.99 & 0.00 \\ \hline SI\_C1 & 0.00 & 1.25 & \(-\)3.03 & 4.44 & \(-\)0.94 & \(-\)0.06 & 0.85 & 30.96 & 0.00 \\ \hline SI\_C2 & 0.00 & 1.14 & \(-\)2.62 & 4.29 & \(-\)0.92 & \(-\)0.07 & 0.72 & 65.62 & 0.00 \\ \hline SI\_C3 & 0.00 & 0.86 & \(-\)2.99 & 3.22 & \(-\)0.63 & 0.05 & 0.60 & 8.34 & 0.02 \\ \hline POS\_C1 & 0.00 & 0.30 & \(-\)0.34 & 1.00 & \(-\)0.34 & 0.00 & 0.15 & 101.9 & 0.00 \\ \hline POS\_C2 & 0.00 & 0.20 & \(-\)0.49 & 0.72 & \(-\)0.12 & \(-\)0.04 & 0.15 & 19.65 & 0.00 \\ \hline POS\_C3 & 0.00 & 0.18 & \(-\)0.29 & 1.31 & \(-\)0.09 & \(-\)0.01 & \(-\)0.01 & 1267.03 & 0.00 \\ \hline CPA\_C1 & 0.00 & 0.15 & \(-\)0.28 & 0.67 & \(-\)0.12 & \(-\)0.03 & 0.12 & 87.94 & 0.00 \\ \hline CPA\_C2 & 0.00 & 0.11 & \(-\)0.28 & 0.46 & \(-\)0.08 & \(-\)0.02 & 0.07 & 104.18 & 0.00 \\ \hline CPA\_C3 & 0.00 & 0.10 & \(-\)0.35 & 0.47 & \(-\)0.06 & \(-\)0.01 & 0.05 & 78.98 & 0.00 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Descriptive statistics and normality test results of lexical features.  
multicollinearity test on lexical features. These features' correlations and clustering were determined based on Pearson correlation coefficients and dendrogram clustering. The dendrogram clustering enables visualizing lexical features that are more similar together, revealing patterns of lexical features that might not have been identified by correlation analysis. The adequate number of clusters depends on the dendrogram's hierarchy. We accordingly grouped the lexical features into three clusters: (POS_C3, CL, SN, SI_C2, POS_C2, and CPA_C2), (FOU, SI_C1, and CPA_C1), and (SI_C3, POS_C1, and CPA_C3). Although these clusters may not definitively distinguish the feature groups, they do offer clues about strong relationships among some features (e.g., the association between CL and SN; the semantic associations among POS, CPA, and SI). We observed the strongest association between SI_C1 and CPA_C1, with a coefficient of 0.726. Multicollinearity did not appear to be an issue in this study.