# Toward a Multi-Column Knowledge-Oriented Neural Network for Web Corpus Causality Mining
###### Abstract
Deep networks play a key role in encoding the linguistic nature of words into fixed-size vectors to lessen the dependence on NLP toolkits [36] by using pre-trained word embedding. A key component of many NLP strategies, pre-trained word embedding offer a number of advantages over embedding taught from scratch [37]. However, due to the causality ambiguity and implicit nature in web corpora, it is still beyond the scope of DL techniques. To mine implicit and ambiguous causality in the web corpora, we proposed a novel deep MCKN model that outperforms the state-of-the-art in terms of its ability to manage the causality problem. However, most of the current approaches are strict when it comes to autonomously engineering features in implicit and ambiguous datasets. As a result, the proposed MCKN is based on a multi-knowledge-oriented channel by parsing every word in the source segments and connective (AltLex) and then identifying causation between the segments on both sides of the connective. According to the proposed paradigm, each channel has a specific input segment or connective that presents a particular set of KCs. Each channel can incorporate linguistic knowledge of cause-and-effect relationships from world knowledge bases (WordNet, FrameNet) by capturing important linguistic cues of causality at the segment and connective level. Each channel uses a variety of convolutional word filters with different window sizes to create numerous unique feature maps.. Utilizing max pooling, the convolution results of each filter are further aggregated, and the feature map is mined for the most promising features that can be used for classification. The usage of \"wf\", a pre-trained word embedding generated automatically by Algorithm 1 from the \"Bootstraps\" corpus, lowers the overall dimensionality of the proposed model. After max pooling, the feature maps of each channel are combined, and dimensionality reduction is used to reduce the dimensionality of the combined feature maps. In the end, four object pairs were produced and sent to RN for further processing since RN needs object pairs for relation reasoning. For the same purpose, we also apply WordNet categorical approaches, \"wf\" selection techniques, FrameNet causal scores, and clustering algorithms for redundant and non-discriminative features. The goals and contributions of our research are described in the section below. ``` Step 1: Find all the lexical units of 50 causal semantic frames from FrameNet and group them by the number of words (max: 64). \(lu_{1}=\{C_{1},C_{2},\ldots,C_{n1}\}\) \(lu_{2}=\{[C_{11},C_{12}],[C_{21},C_{22}],\ldots,[C_{n1},C_{n2}]\}\) \(lu_{3}=\left\{[C_{11},C_{12},C_{13}],[C_{21},C_{22},C_{23}],\ldots,\left[C_{n 1},C_{n2},C_{n3}\right]\right\}\) \(lu_{4}=\left\{[C_{11},C_{12},C_{13},C_{14}],[C_{21},C_{22},C_{23},C_{24}], \ldots,\left[C_{n1},C_{n2},C_{n3},C_{n4}\right]\right\}\) \(lu_{5}=\left\{[C



* Extensive experiments on publicly available datasets have shown that the MCKN model performs much better than many baseline methods and text classification techniques. The remainder of this article is structured as follows. Section 2 presents the literature review. Section 3 details the suggested strategy. The entire experimental process is covered in detail in Section 4. Finally, Section 5 summarizes our conclusion. ## 2 Literature Review

In terms of causality mining, previous research has mainly been divided into ML and DL methods. The performance gain of DL over ML techniques is significant. ML approaches normally require sophisticated feature engineering. For ML approaches, Ref. [38] uses a dependency structure to derive causation event pairs. In [39], causal connectives were used to govern how lexico-syntactic patterns and causal connectives interacted. These connectives were obtained by computing the similarity of sentence syntax-dependent structures through the Restricted Hidden Naive Bayes (RHNB) classifier.