# Toward a Multi-Column Knowledge-Oriented Neural Network for Web Corpus Causality Mining
###### Abstract  
In the digital age, many sources of textual content are devoted to studying and expressing many sorts of relationships, including employer-employee, if-then, part-whole, product-producer, and cause-effect relations/causality. Mining cause-effect relations are a key topic in many NLP (natural language processing) applications, such as future event prediction, information retrieval, healthcare, scenario generation, decision making, commerce risk management, question answering, and adverse drug reaction. Many statistical and non-statistical methods have been developed in the past to address this topic. Most of them frequently used feature-driven supervised approaches and hand-crafted linguistic patterns. However, the implicit and ambiguous statement of causation prevented these methods from achieving great recall and precision. They cover a limited set of implicit causality and are difficult to extend. In this work, a novel MCKN (multi-column knowledge-oriented network) is introduced. This model includes various knowledge-oriented channels/columns (KCs), where each channel integrates prior human knowledge to capture language cues of causation. MCKN uses unique convolutional word filters (wf) generated automatically using WordNet and FrameNet. To reduce MCKN's dimensionality, we use filter selection and clustering approaches. Our model delivers superior performance on the Alternative Lexicalization (AltLexes) dataset, proving that MCKN is a simpler and distinctive approach for informal datasets. causality mining; knowledge-oriented channels; multi-level knowledge-oriented network; relation network; relation classification +
Footnote †: journal:  
sense [17]. The idea has long been up for debate among philosophers. The sociological theory dictionary [18] provides the following standard definition:  
* When one event or series of related events occurs first and paves the way for the occurrence of subsequent events. When the initial event (cause) occurs, the second event (effect) inherently or definitely follows. * According to the theory of multiple causations, there are numerous possible causes for a particular event, each of which might be sufficient but not necessary for the effect to occur or necessary but insufficient for the effect to exist. Causality mining emphasizes the automatic detection/extraction of causality between events in the text. As an example, \"The 2019 COVID-19 pandemic caused a series of shocking deaths around the world\" implies causality between the cause (COVID-19 pandemic) and the effect (deaths). Numerous methods for establishing causation are listed in the literature, including causal knowledge and causal discovery [19; 20]. However, earlier attempts at causality mining relied on machine learning and rule-based methods. Rule-based methods, however, require carefully planned linguistic features [13; 15; 21; 22]. Usually, these methods overlook hidden features and sequences of events. They are only able to mine domain-dependent explicit causality from phrases, and they do not take into account the features that point to the presence of dependence relationships. Similar to this, in machine learning approaches [4; 23; 24; 25], semantic, syntactic, and lexical features are constructed by human operators through diligently feature engineering, and causality is automatically determined from large labeled datasets. The model's effectiveness in these techniques depends on the arrangement and regularities of its features. However, the absence of annotated datasets restricts these methods, which leads to error propagation in the systems. Currently, the rising demand for the deep neural network such as RNN [26; 27], CNN [28], MCNN [29], Transformer block [30], BERT [31], TinyBERT [32], and Hopfield neural network (HNN) [33; 34; 35] make it possible to perform various processing tasks without complex feature engineering.