# Causal Pathway Extraction from Web-Board Documents
## 4 System Overview
### Implicit-Mediator Indication and Representation with Explicit-Mediators
The test corpus

Figure 7: The representation of the correct extracted causal pathways with the explicit mediators of the diabetes and kidney disease group where the numeric label on each arrow represents the CErel probability of each CEpair by NB. consists of 2000 EDUs from the diabetes and kidney disease group documents and the 2000 EDUs from the heart and artery disease group documents. There are three evaluations, (1) the determination of wrdCoc pairs having CErel, (2) the causal pathway extraction, and (3) the implicit-mediator indication and representation with the explicit mediators from the dynamic template. ### Determination of wrdCoc Pairs Having CErel

The evaluation results of extracting the EDU-concept pairs/wrdCoc pairs having CErel from the documents of the diabetes and kidney disease group and the heart and artery disease group are the precisions and the recalls based on three experts with max win voting as shown in Table 3 including the number of different wrdCoc features which results in the frequencies of wrdCoc features as shown in Figure 8.

From Table 3, the average precisions of extracting wrdCoc pairs having CErel from the documents of the diabetes and kidney disease group and the heart and artery disease

Figure 8: Show the wrdCoc frequencies with the causative concepts and the effect concepts from the diabetes and kidney disease group and the heart and artery disease group. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Disease Group (2000**} & \multicolumn{2}{c}{**\#of Different**} & \multicolumn{6}{c}{**Extraction of wrdCoc Pairs Having CErel**} \\ \cline{2-9}  & **wrdCoc Features** & \multicolumn{3}{c}{**NB**} & \multicolumn{3}{c}{**SVM**} & \multicolumn{3}{c}{**LR**} \\ \cline{2-9}  & **Cause** & **Effect** & **Precision** & **Recall** & **Precision** & **Recall** & **Precision** & **Recall** \\ \hline Diabetes and kidney disease group & 40 & 92 & 0.844 & 0.777 & 0.893 & 0.803 & 0.877 & 0.795 \\ \hline Heart and artery disease group & 93 & 110 & 0.826 & 0.746 & 0.841 & 0.760 & 0.831 & 0.754 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The accuracy of determining wrdCoc pairs having CErel. group are 0.871 and 0.833 respectively, with the average recalls of 0.791 and 0.753 and the average F-score of 0.830 and 0.791 respectively. Whereas the causality or cause-effect relation extraction from the previous research [7] based on the probabilities of words on NP pair and the cue phrase probability from the complex sentence or two simple sentences from the medical domain has an F-score of 0.774. With regard to our research results on Table 3, the reason for the diabetes and kidney disease group having the higher precision and recall of extracting wrdCoc pairs having CErel than the heart and artery disease group is that the heart and artery disease group have more diversity of the wrdCoc features in both the causative concepts and the effect concepts than the diabetes and kidney disease group. The high diversity of wrdCoc features results in low frequencies of most wrdCoc features. In addition, there are some dependency occurrences among wrdCoc features, e.g.,

EDU1: 'haveHyperglycaemia(person)' \(\rightarrow\) EDU2:'becomeInflamed(BloodVessel)', where EDU1's wrdCoc and EDU2's wrdCoc mostly occur as a cause-effect relation or a dependency occurrence on documents but some documents contain EDU1's wrdCoc followed by EDU2's wrdCoc without the cause-effect relation. Thus, the wrdCo diversity and the wrdCoc dependency result in SVM having highest precision in both the diabetes and kidney disease group and the heart and artery disease group. However, both the diabetes and kidney disease group and the heart and artery disease group have low recalls because the diversity of wrdCo expressions occurs on the downloaded documents of both disease groups.