# Representing Aspectual Meaning in Sentence: Computational Modeling Based on Chinese
## 3 Results and Discussion  
### Semantic Distance Visualization and Hierarchical Clustering  
After performing cross-validation across the three datasets and taking the intersection of consistent annotation results, the number of verb senses was determined to be 1610.  
The senses of verbs with the same distribution in terms of their abilities to co-occur with different aspectual markers have the same situation type, and thus, they are merged before the hierarchical clustering. Typical words are picked out as the representatives of these groups including type shi (be), type ren4 shi (know), type xi3 huan1 (like), type xin4 ren4 (trust), type ai4 (love), type gong1 zuo4 (work), type chi1 (eat), type chan3 sheng1 (generate), type li2 kai1 (leave) and type si3 (die).  
We use PCA (Principle Components Analysis) to reduce the dimensions, which is shown in Figure 2.  
As the number of components increases, the proportion of explained variance keeps rising, with two components achieving 87% and three components reaching 94%, respectively, which makes it reasonable to visualize the semantic distance between senses of verbs in a three-dimension coordinate system or vector space. The coordinate system is shown in Figure 3.  
The graph shows that several types are not distant from each other. Type xi3 huan1 (like), type ai4 (love), type xin4 ren4 (trust) and type ren4 shi2 (know) are close; type chi1 (eat) and type gonglzuo4 (work) are just next to each other; type chan3 shengq (generate), type li2 kai1 (leave) and si3 (die) are also not far from each other. It should be noted that si3 (die) seems to be isolated. However, this is caused by the projecting angle. Type shi4 (be), although it only contains two verbs, is separated from other groups.  
With a CPCC of 0.77, the method of centroid is applied to generate the dendrogram in Figure 4 and the clustered result is consistent with the three-dimensional graph.  
Figure 3: Positions of different types of verbs in aspectual vector space.  
Figure 2: The explained variance ratio of different numbers of PCA components.  
Type ai4 (love), xin4 ren4 (trust), xi3 huan1 (like) and ren4 shi2 (know) are merged into the first group from the right. It is clear that type ai4 (love) and xin4 ren4 (trust) are further merged into one branch. Type xi3 huan1 (like) and ren4 shi2 (know) form another branch in this group. The senses of this type of verbs express mental states or mental activities. This group is thus called state verb.
* Type chi1 (eat) and type gonglzuo4 (work), which are typical activity verbs, are grouped together. We assign the situation type of activity to this group.
* Type si3 (die), li2 kai1 (leave) and chan3 sheng1 (generate), which illustrate a transition or change in state, are clustered into one group. As a result, this group is named transition.
* Type shi4 (be), which represents a kind of attribute, is isolated to be one individual group, including several verbs such as shi4 (be) and deng3 yu2 (equal to). Because of the small sample size, these verbs are merged into the first situation type, which is state.  
By clustering verb senses into distinct situation types, we can assign corresponding tags to each verb and its senses, as illustrated in Table 2. Among the 1610 verb senses analyzed, activity-related senses dominate, representing over 60% of the total. In contrast, transition senses are the least frequent, while state senses fall somewhere in between these two extremes.