# Web Page Content Block Identification with Extended Block Properties
## 3 Block Identification Method for Block Range and Variation Estimation
### Results of Web Page Division to Content Blocks Comparison to Manual Labeling  
The web page division to content blocks for manual and automated labeling were stored identically (except the label was not set in automated segmentation) in the same database structure but in different instances of it. Due to the database structure matching, a comparison of manual and automated division is possible. On the other hand, it is not a straightforward process, as in manual labeling, the user could identify some tags and content blocks but not others. The grouping of tags into maximum and minimum boundary blocks was not requested either. Therefore, additional methods were prepared to match a manually labeled tag to an automated division to content block with an estimation of whether the manually labeled tag fits within the content block boundaries (minimum bounds <= labeled tag <= maximum bounds). By using this method, we can estimate the match between labels, even in cases where the boundaries are labeled similarly but not identically. Another method estimates whether a labeled tag corresponds to another content block based on its structure. The feature gathers data about sibling blocks, which were ignored for simplicity, however, correspond to some of the already identified blocks but with different content. This matching between automated web page division to content blocks and manual labeling enabled us to estimate whether our solution was able to identify all content blocks labeled by humans. In total, 1179 content blocks were labeled by human experts (1104 WYT). The dependency of a number of tags in the web page and our method of identified blocks are presented in Figure 6 which indicates the linear dependency. The labeled blocks were mapped to the identified by using our proposed content block identification method. For further performance analysis, the standard classification metrics were used. The true positive (TP) was assumed for the number of blocks, indicated by our method and matching the dataset-defined segments or user-labeled blocks. False positive (FP) were the other blocks our method detected, but which were not labeled in the dataset. False negative (FN) was for a number of blocks which were labeled in the dataset but were missing in our output, while the true negative (TN) was calculated by subtracting TP, FP, and FN from the total number of tags in the web page. The summary of the metrics is presented in Table 4.  
The results indicate that the proposed web page division to content blocks solution can identify all content blocks that would be manually labeled. At the same time, it identifies additional content blocks that were ignored during manual labeling. The main reason for ignoring some blocks during manual labeling is their repetitive nature. This repetitiveness can be observed in a couple of ways. First, when a block can have various boundaries while presenting the same content, we use the block's max-min boundary detection to negate the need to label all of the possible combinations of the same block. Another situation with repetitive blocks arises when there are multiple content blocks for the same purpose but with different content. The most basic example of this is the navigation menu. Each menu element has the same structure as all the other elements of the same menu, so users tend to mark only one menu element. We use structural sibling relationships between blocks to detect other menu elements. This allows us to detect all menu items regardless of which menu element was labeled by the user. Sometimes, these two cases of repetitiveness can