# Representing Aspectual Meaning in Sentence: Computational Modeling Based on Chinese
## 3 Results and Discussion
### Statistical Validation of Classification Model  
After matching the senses of verbs, together with their situation types, to the verb-tag pairs in SINICA corpus, it is applicable to validate the reliability of the dataset containing verbs and their situation types in a corpus by using multi-nominal logistic regression analysis.  
It is obvious that the total number of items in Table 3 is smaller than that in Table 2 due to several reasons. On the one hand, not all verbs can be found in the SINICA corpus and it is the same case for verbs' senses; on the other hand, some of the senses in Meng [22] can be mapped into one item in SINICA without changing their situation types.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **State** & **Activity** & **Transition** & **Total** \\ \hline Number & 387 & 978 & 245 & 1610 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Distribution of verbs’ situation types in Meng [22].  
Figure 4: Hierarchical clustering of different types of verbs.  
Multicollinearity test is supposed to be conducted before the regression analysis since it may bring in model distortion. Multicollinearity test is implemented through correlation analysis. If data follow normal distribution, Pearson correlation analysis is appropriate. If not, Spearman correlation analysis should be used.  
We use Shapiro-Wilk test to confirm whether our data follow normally distribution or not [26].  
Table 4 shows that none of these co-occurrence frequencies follow a normal distribution, and thus, Spearman correlation analysis should be used to test the multicollinearity.  
The multicollinearity test results confirm that all co-occurrence frequencies are suitable for logistic regression analysis. As evidenced by the data, the highest correlation coefficient in Table 5 stands at 0.18, while Table 6 reveals a \(p\)-value approaching zero. These findings collectively demonstrate the absence of significant correlations among the co-occurrence frequencies, thereby validating their independent use in the subsequent analysis.  
The LLR p-value with a value of zero in Table 7 confirms the reliability of the dataset, and it proves that our manual annotation of situation types for verbs (senses) in our dataset can be directly used for linguistics and computational studies.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Aspectual Markers** & **ZHE** & **LE1** & **LE2** & **GUO** & **ZAI** & **ZHENGZAI** \\ \hline ZHE & 0.000000 & 0.204379 & 0.000024 & 0.000008 & 0.000003 & 0.111585 \\ LE1 & 0.204379 & 0.000000 & 0.333467 & 0.503474 & 0.155270 & 0.363621 \\ LE2 & 0.000024 & 0.333467 & 0.00000 & 0.000924 & 0.003146 & 0.721551 \\ GUO & 0.000008 & 0.503474 & 0.000924 & 0.000000 & 0.00000 & 0.000000 \\ ZAI & 0.000003 & 0.155270 & 0.003146 & 0.000000 & 0.000000 & 0.000000 \\ ZHENGZAI & 0.111585 & 0.363621 & 0.721551 & 0.000000 & 0.000000 & 0.000000 \\ \hline \hline \end{tabular}
\end{table}
Table 6: \(p\)-values of the coefficients.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Aspectual Markers** & **W** & \(p\)**-Value** \\ \hline ZHE & 0.01118 & 0.0000 \\ LE1 & 0.02165 & 0.0000 \\ LE2 & 0.1097 & 0.0000 \\ GUO & 0.1049 & 0.0000 \\ ZAI & 0.1014 & 0.0000 \\ ZHENGZAI & 0.0876 & 0.0000 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Shapiro-Wilk test on aspectual markers’ co-occurrence frequencies.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Aspectual Markers** & **ZHE** & **LE1** & **LE2** & **GUO** & **ZAI** & **ZHENGZAI** \\ \hline ZHE & 1 & \(-\)0.03636 & 0.120529 & 0.127262 & 0.133219 & 0.045576 \\ LE1 & \(-\)0.03636 & 1 & 0.027713 & 0.019172 & \(-\)0.04071 & \(-\)0.02603 \\ LE2 & 0.120529 & 0.027713 & 1 & 0.094726 & 0.084482 & 0.010214 \\ GUO & 0.127262 & 0.019172 & 0.094726 & 1 & 0.143605 & 0.162479 \\ ZAI & 0.133219 & \(-\)0.04071 & 0.084482 & 0.143605 & 1 & 0.177979 \\ ZHENGZAI & 0.045576 & \(-\)0.02603 & 0.010214 & 0.162479 & 0.177979 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Spearman correlation coefficients between aspectual markers.