# A Novel Approach to Data Extraction on Hyperlinked Webpages
## 4 Results
### Table Classification  
The CRF model was espoused to render labels for the web table rows. As explained above, the CRF classification produced the sequence of labels for candidate table rows. This sequence was parsed to self-defined automaton-NFA which contained frequent possibilities upon which a complex table could be classified as a real table. The automaton-NFA is graphically depicted in Figure 4. We used 24,000 (80%) tables for training and 6000 (20%) tables for testing purposes. Of 21,000 real tables, 14,280 (68%) were predicted correctly (true positive), whereas, of 9000 non-real tables, 8940 (99.3%) were correctly identified. This has been shown in the confusion matrix (Table 6).  
Precision, recall, F-measure, accuracy, and specificity were calculated to scrutinize the potential of the applied framework. The following notation was used:  
**True positive (TP)** = the number of cases correctly identified as real.  
**False positive (FP)** = the number of cases incorrectly identified as real.  
**True negative (TN)** = the number of cases correctly identified as non-real.  
**False negative (FN)** = the number of cases incorrectly identified as non-real  
**Precision:** Exactness: the precision is a fraction of relevant instances among the retrieved instances (1).  
\[\text{Precision }(\text{P})\ =\ \frac{\text{TP}}{\text{TP}+\text{FP}}. \tag{1}\]  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Actual Class\textbackslash{}Predicted Class** & **Real Tables** & **Non-Real Tables** & **Total** \\ \hline Real Tables & 14,280 (68\%) & 6720 (32\%) & 21,000 \\ Non-Real Tables & 60 (0.7\%) & 8940 (99.3\%) & 9000 \\ Total & 14,340 & 15,660 & 30,000 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Confusion matrix.  
Figure 4: Automaton construction.  
In our case, the precision value reports the number of tuples that the classifier labeled as positive that are actually positive. This measure determines how well the classifier has classified relevant tables only, and does not retrieve any non-relevant items from the dataset. Our precision was calculated at 0.99.  
**Recall:** Completeness: the recall is the fraction of relevant instances that have been retrieved over total relevant instances in (2)  
\[\text{Recall }(\text{R})\ =\ \frac{\text{TP}}{\text{TP}+\text{FN}}. \tag{2}\]  
In our case, the recall value describes how well our classifier does at classifying all the tables in the dataset that have been determined to be topically relevant. The formal representation of precision is presented in Equation (2). Our recall value was 0.68.  
**Sensitivity:** This measure is a true positive recognition rate, which measures the proportion of actual positives that are classified correctly by the classifier. The formal representation of precision is presented in Equation (3). The system achieved a sensitivity of 0.68.  
\[\text{Sensitivity }(\text{S})=\frac{\text{TP}}{\text{P}}. \tag{3}\]  
**Specificity:** It reports a true negative recognition rate, which means the proportion of actual negatives that are classified correctly by the classifier. The formal representation of precision is presented in Equation (4). Our specificity was 0.99.  
\[\text{Specificity }(\text{Sp})=\frac{\text{TN}}{\text{N}}. \tag{4}\]  
**Accuracy:** The accuracy of a classifier is its ability to differentiate between relevant and non-relevant classes correctly. The system achieved an accuracy of 0.77. The value reports the percentage of test set tuples that are correctly classified by a classifier. The formal representation of precision is presented in Equation (5).  
\[\text{Accuracy }(\text{A})=\frac{\text{TP}+\text{TN}}{\text{All}}. \tag{5}\]  
**F-measure:** It is a means of precision and recall. The formal representation of F-measure is presented in Equation (6). The system achieved a F-measure of 0.80  
\[\text{F-measure }=\ 2*\ \frac{\text{precision }*\ \text{recall}}{\text{ precision }+\ \text{recall}}. \tag{6}\]