# Toward a Multi-Column Knowledge-Oriented Neural Network for Web Corpus Causality Mining
###### Abstract
The latest work [55] uses a head-to-tail entity annotation method that expresses the entire semantics of complex cause-effect relationships and visibly finds entity boundaries in source sentences. They employ entity location perception along with RPA-GCNs (Relation Position and Attention-graph Convolutional Networks), GATs (Graph Attention Networks), and other techniques. In [56], a generative approach for extracting cause-effect relationships via encoder-decoder and pointer networks. They enhanced the performance but required more time to produce the required result. In comparison to statistical and non-statistical techniques, DL approaches with pre-trained word embedding are more fruitful. However, they work on a huge training dataset that covers all causality expressions in the source text, which is somewhat impossible due to the diversity and ambiguity of phrases and words in the dataset. However, the ambiguous and implicit nature of causality is a challenging task. Our MKN is motivated and inspired by [26; 29; 31; 57] for mining implicit and ambiguous causality sentences from publically available web corpora. To begin, keep in mind that previous methods for leveraging MCNNs for NLP applications used multiple CNN channels with pre-defined convolutional filters for training. Our inspiration is a novel approach using the concept of the MCNN approach. Contrary to MCNNs, the proposed approach is based on knowledge-oriented channels by using novel convolutional word filters generated by Algorithm 1. Table 1 provides a more concise description of the reviewed material. \begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**References** & **Description** & **Features** & **Benefits** & **Drawbacks** \\ \hline \multirow{3}{*}{[38]} & Using a supervised approach for extracting causality & Using event 1 cause, event 2 patterns & First attempt toward web corpus & Focused on explicit domain- specific causality \\ \hline \multirow{3}{*}{[22]} & Mining AltLexes of causal discourse relations & Using Explicit discourse connectives & Target web coups and Action of AltLexes dataset & Working with a large feature vector set typically slows down the processing of the model \\ \hline \multirow{3}{*}{[29]} & An approach based on MCNNs [48] & Using prior BK features & Targeted implicit and ambiguous causality & Japanese dataset was emphasized \\ \hline \multirow{3}{*}{[39]} & RHNB classifier & RHNB model based patterns & Combine lexi-cosyntactic pattern and causality connective in one place & Target large features that typically slow down model performance \\ \hline \multirow{3}{*}{[40]} & Conditional text generation network & Using generated patterns & Bypass human effort & Emphasized explicit relationships within individual sentences \\ \hline \multirow{3}{*}{[8]} & Attention based MCNNs & MCNNs + Attention & Inspiration toward causality attention & For QA using causality attention \\ \hline \multirow{3}{*}{[49]} & Computing the distance among events triggering words and related words in phrases events & \multirow{3}{*}{FFNN based in phrases events} & They targeted implicit and ambiguous causalities & Over-fitting problem \\ \hline \hline \end{tabular}
\end{table} 
Table 1: A list of related techniques that have been examined. ## 3 Proposed Approach

This section explores the MCKN model. This model consists of three channels/columns, where each channel deals with its respective AltLex/connective (L), segments after AltLex (AL), and segments before AltLex (BL) in the target sentence. MCKN mainly targeted implicit and ambiguous causalities. The MCKN uses convolutional word filters instead of pre-trained convolutional filters. In Figure 1, we explored the architecture of the MCKN model, including (i) the first column dealing with the BL (e1) part of the input sentence, (ii) the second column dealing with the L part of the input sentence, and (iii) the third column dealing with the AL (e2) part of the input sentence. More details about Figure 1 are covered in Section 3.4.