# Toward a Multi-Column Knowledge-Oriented Neural Network for Web Corpus Causality Mining
###### Abstract
**Evaluation Metrics:** We compare MCKN with the baseline techniques in Table 3 using a variety of evaluation metrics, such as precision, recall, and F1-score. The prediction ability of algorithms is measured by their precision (_Pr_). It illustrates how many positive predictions are achieved and how accurate predictions are made by individuals who make them. The '_Pr_' is calculated in Equation (11). Among these, true positive (_TP_) is the number of correctly classified positive cases, and true negative (TN) is the proportion of correctly classified negative events. False positive (_FP_) refers to the number of positively classed instances that were misclassified, while false negative (_FN_) refers to the number of positively classified instances that were incorrectly classified. \[Pr=\frac{TP}{TP+FP} \tag{11}\]

\(F\)-score (F1) is a crucial need for simulating the situation with the highest probability of obtaining the correct answer and explicitly demonstrating the algorithm's ability. Moreover, F1-score is defined as a harmonic mean of sensitivity and precision. The \(F\) value is calculated in Equation (12). \[F=\frac{2 TP}{2 TP+FP+FN} \tag{12}\]

Recall (_Rc_) or sensitivity examines how well a case accurately yields a positive outcome for an instance that has an explicit condition. Equation (13) calculates the value of _Rc_. \[Rc=\frac{TP}{TP+FP} \tag{13}\]

### Baseline Methods

In this section, various baseline approaches are listed, including MCNN, K-CNN, DPCNN, and BERT-base. DPCNN [57] is a word-level deep neural network for topic categorization and sentiment classification. It can create downsampling without increasing the number of feature maps, which can efficiently represent long-range relationships. A deep pre-trained language representation system called BERT-base [31] is built on masked and Transformer blocks, and it has improved a number of NLP applications, encouraged by transfer learning from the computer vision sector. The next notable work in this field is MCNN [29], a multi-column CNN with BK that integrates event causality candidates and their contexts with relative web corpus. K-CNN [26] is the next novel work, which combines a data-oriented network with a knowledge-oriented network by using convolutional \"wf\", thereby reducing the overall dimension of the model. ### Results

Before releasing the results, we run each reproducible experiment six times for causality extraction using a train/bootstrapped/Dev test split described in Section 4.1. Then, we report the average result along with its standard deviation. Table 3 compares MCKN's performance with state-of-the-art methods employing precision, recall, and F1-score in the test set, which is a randomly selected subset of both the train and bootstrapped datasets. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{3}{c}{**Training Dataset**} & \multicolumn{3}{c}{**Bootstrapped Dataset**} \\ \cline{2-7}  & **Precision** & **Recall** & **F1-Score** & **Precision** & **Recall** & **F1-Score** \\ \hline K-CNN [26] & 80.57 & 43.17 & 53.54 & 78.45 & 73.97 & 76.14 \\ \hline DPCNN [57] & 69.06 & 58.10 & 63.10 & 79.66 & 74.60 & 77.05 \\ \hline BERT-base [31] & 68.97 & 63.49 & 66.12 & 75



Our model performs, in particular, by learning distinct semantic representations of causation at the connective and segmental levels. In the train dataset, compared with the best state-of-the-art feature engineering methods [26; 29; 31; 57], MCKN enhanced the maximum precision by 21.58% and a minimum of 5.42%, F1-score recorded by a maximum of 33.27% and a minimum of 2.91%, and similarly, a low recall rate is recorded 0.48%, since it emphasizes on the interchangeability of connectives, whereas parallel examples frequently contain the same connectives that might be evaluated as false negatives. It is amazing that the proposed work on the bootstrapped train dataset enhances the precision up to a maximum of 17.14% and a minimum of 12.67%, the F1 score of a maximum of 14.01% and a minimum of 2.64%.