# Toward a Multi-Column Knowledge-Oriented Neural Network for Web Corpus Causality Mining
###### Abstract
In Figure 6, \(y\)-axis signifies the score of the Precision-Recall Curve (AUPRC), Areas under the Receiver Operator Curve (AUROC), and F-Score to estimate the specificity and sensitivity of the model. Figure 5: Bootstrapped dataset: relationship between number of epoch and performance. #### 4.5.3 Ablation Study

Exploring MCKN and its contributions is very important to readers. In this section, we show the ablation evaluation through different training modules of the proposed model. Table 5 describes the results of the different modules of MCKN on the two datasets. In the training dataset, the single-column KNN + RN module reaches the precision (\(p\)) value of 78.74, the recall (R) value is 76.56, and an F1 Score (F-1) is 73.85. The two-column KNN+RN has enhanced the \(p\) value by 4.38, the R-value by 2.77, and the F-1 value by 7.54; further, the three-column KNN + RN module enhanced the \(p\) value of 7.43, the R-value of 3.89, and the F-1 value of 5.42 compared to two-column KNN + RN. Similarly, in the bootstrapped dataset, the single-column KNN + RN module reached a \(p\) value of 79.23, an R-value of 82.11, and an F-1 of 80.21. In the two-column KNN + RN, the \(p\) value is enhanced by 7.96, the R-value by 2.11, and the F-1 value by 5.6, of which the three-column KNN + RN module further enhanced the \(p\) value of 5.94, the R-value 7.57, and the F-1 value 4.34 compared to two-column KNN + RN. Based on the above analysis, compared with the single KNN + RN and two-column KNN + RN, the three-column KNN + RN (MCKN) shows significant results, because the three-column KNN + RN uses the combined features and knowledge of all channels. This demonstrates how multi-column KNNs and RN significantly boosted the model's overall performance. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Types** & **AUROC** & **AUPRC** & **F1-Score** \\ \hline GloVe-840B & 87.48 & 88.58 & 83.50 \\ \hline Pre-trained Wiki & 85.32 & 84.11 & **85.23** \\ \hline Google News & 86.30 & 87.37 & 83.90 \\ \hline Average & 86.80 & 87.29 & 84.90 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance analysis of pre-trained-word embedding. Figure 6: Analysis of different word embedding. ## 5 Conclusions

The novelty of this work is how to recognize ambiguous and implicit causality in the informal \"AltLexes\" web corpus. When compared to online corpora, the majority of earlier works used more formal newspaper, historical stories, and book corpora that incorporated clear causation. They frequently employ feature-driven supervised techniques to target explicit causality and overlook the implicit and ambiguous causation in the web corpus. In this work, a novel MCKN model is proposed that combines more than one KC and is integrated with RN for causality extraction in the unstructured web corpus. MCKN deals with each sentence at the connective and segment level for causal relational reasoning. The proposal employs a new convolutional word filter approach that drastically reduces the number of model parameters. Our model demonstrates the power of inferring complicated causation at the sentence level, in contrast to causality and document classification algorithms. Although, implicit and ambiguous causality and their event pair detection across sentences/multi-sentence text is still a demanding problem. For such task in future development, it is imperative to employ this model with more advanced features and a standardized dataset. Conceptualization, W.A. ; Methodology, W.A. ; Software, W.A. ; Formal analysis, W.A., W.Z., Y.W. and R.A.; Investigation, W.Z. ; Writing--original draft, W.A. ; Writing--review & editing, Y.W. and R.A.; Supervision, W.Z. ; Funding acquisition, W.Z. All authors have read and agreed to the published version of the manuscript. This work is supported by the National Natural Science Foundation of China (No. 61976103, No. 62272191), the Science and Technology Development Program of Jilin Province (No. 20220201153 GX). Not applicable.