# Causal Pathway Extraction from Web-Board Documents
## 4 System Overview
### Determination of wrdCoc Pairs Having CErel  
The WC elements are determined from all wrdCo expressions on the test corpus of each disease group by the wrdCo-expression matching between the wrdCo expressions on this test corpus and the wrdCo expressions on wrdCo-Concept Table (Table 1) to obtain the wrdCoc features or the WC elements. The result of the self-Cartesian product (WC \(\times\) WC) is a wrdCo-concept ordered pair set which is used for determining and collecting the wrdCoc pairs having CErel into WCP of each disease group by the following NB, SVM, and LR. * NB. Regarding Equation (5) and the CErel and nonCErel probabilities of CwrdCoc and EwrdCoc (Table 2), the CwrdCoc EwrdCoc pairs as the wrdCoc pairs having Cause-EffectRelationClass as CErel is determined from the self-Cartesian product (WC \(\times\) WC) result and then collected into WCP of each disease group on which CwrdCo and EwrdCoc are independent. \begin{table}
\begin{tabular}{l l c c c c c} \hline \hline
**Disease** & **CwrdCoc** & **CErel** & **Noncerel** & **EwrdCoc** & **CErel** & **Noncerel** \\ \hline \multirow{4}{*}{Diabetes \& Kidney() & \textless{beLost(protein,urine)}\textgreater{} & 0.0698 & 0.0784 & \textless{beLost(protein,urine)}\textgreater{} & 0.0074 & 0.0066 \\  & \textless{beFailure(kidney)}\textgreater{} & 0.1581 & 0.0784 & \textless{beFailure(kidney)}\textgreater{} & 0.0355 & 0.0333 \\ \cline{1-1}  & Kidney & \textless{haveHyperglycaemia} & 0.0452 & 0.0294 & \textless{haveHyperglycaemia} & 0.0411 & 0.0200 \\ Disease & (person)\textgreater{} & & & (person)\textgreater{} & & \\ Group & \textless{notTakeSugar(body)}\textgreater{} & 0.0266 & 0.0392 & \textless{notTakeSugar(body)}\textgreater{} & 0.0112 & 0.0133 \\  & \textless{beNarrow(bloodVessel)}\textgreater{} & 0.0185 & 0.0098 & \textless{beNarrow(bloodVessel)}\textgreater{} & 0.0374 & 0.0533 \\  & \(\cdots\cdots\cdots\cdots\) & & & & \(\cdots\cdots\cdots\cdots\cdots\) & \(\cdots\cdots\cdots\) \\ \hline \multirow{4}{*}{Heart \& Kidney() & \textless{beDeposited(fat,}\) & 0.0540 & 0.0288 & \textless{beDeposited(fat,}\) & 0.0011 & 0.0193 \\  & bloodVessel\textgreater{} & 0.0194 & 0.0288 & \textless{beThick(bloodVessel)}\textgreater{} & 0.0169 & 0.0038 \\ \cline{1-1}  & \textless{beDamages(bloodVesselWall)}\textgreater{} & 0.0218 & 0.0041 & \textless{beDamages(bloodVesselWall)}\textgreater{} & 0.0101 & 0.0038 \\ \cline{1-1}  & \textless{becomeInflamed(bloodVessel)}\textgreater{} & 0.1323 & 0.1028 & \textless{becomeInflamed(bloodVessel)}\textgreater{} & 0.0079 & 0.0115 \\ \cline{1-1}  & \(\textless{beClogged(bloodVessel,organ)}\textgreater{}0.0448\) & 0.0288 & \textless{beClogged(bloodVessel,organ)}\textgreater{} & 0.0418 & 0.0424 \\ \cline{1-1}  & \(\cdots\cdots\cdots\cdots\) & & & & \(\cdots\cdots\cdots\cdots\) & \(\cdots\cdots\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Show the CErel and nonCErel probabilities of CwrdCoc features and EwrdCoc features in wrdCoc pairs of the diabetes and kidney disease group and the heart and artery disease group. Cause - EffectRelationClass \(=\underset{class\in Class}{\operatorname{argmax}}\,P(class|CvrdCoc,EwrdCoc)\) \[=\underset{class\in Class}{\operatorname{argmax}}\,P(CvrdCoc|class)P(EwrdCoc|class)P(class)\] where \(CvrdCoc\) is a causative - wrdCo concept; \[EwrdCoc\] is an effect - wrdCo concept; Class = {\"CErel\", \"nonCErel\"}. * SVM. The bias, \(b\), and the weight vector, w, of the CWC elements and the EWC elements in the wrdCoc pairs from the SVM learning (Section 4.2 (b)) are used to determine and collect the CwrdCc EwrdCoc pairs as the wrdCoc pairs having the CErel class into WCP of each disease group from the self-Cartesian product (WC \(\times\) WC) result with Equation (3). * LR the research applies Equation (4) along with Equation (6) to determine the CErel class between the CWC elements and the EWC elements in the wrdCoc pairs from both the positive/CErel class determination and the negative/nonCErel class determination by using the estimators from the LR learning (Section 4.2 (c)).