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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.

As a data augmentation method, masking word is commonly used in many natural language processing tasks. However, most mask methods are based on rules and are not related to downstream tasks. In this paper, we propose a novel masking word generator, named Actor-Critic Mask Model (ACMM), which can adaptively adjust the mask strategy according to the performance of downstream tasks. In order to demonstrate the effectiveness of the method, we conducted experiments on two causal event extraction datasets. Experiment results show that, compared with various rule-based masking methods, the masked sentences generated by our proposed method can significantly enhance the generalization of the model and improve the model performance.

The era of big textual corpora and machine learning technologies have paved the way for researchers in numerous data mining fields. Among them, causality mining (CM) from textual data has become a significant area of concern and has more attention from researchers. Causality (cause-effect relations) serves as an essential category of relationships, which plays a significant role in question answering, future events predication, discourse comprehension, decision making, future scenario generation, medical text mining, behavior prediction, and textual prediction entailment. While, decades of development techniques for CM are still prone to performance enhancement, especially for ambiguous and implicitly expressed causalities. The ineffectiveness of the early attempts is mainly due to small, ambiguous, heterogeneous, and domain-specific datasets constructed by manually linguistic and syntactic rules. Many researchers have deployed shallow machine learning (ML) and deep learning (DL) techniques to deal with such datasets, and they achieved satisfactory performance. In this survey, an effort has been made to address a comprehensive review of some state-of-the-art shallow ML and DL approaches in CM. We present a detailed taxonomy of CM and discuss popular ML and DL approaches with their comparative weaknesses and strengths, applications, popular datasets, and frameworks. Lastly, the future research challenges are discussed with illustrations of how to transform them into productive future research directions.

Global terrorism has created challenges to the criminal justice system due to its abnormal activities, which lead to financial loss, cyberwar, and cyber-crime. Therefore, it is a global challenge to monitor terrorist group activities by mining criminal information accurately from big data for the estimation of potential risk at national and international levels. Many conventional methods of computation have successfully been implemented, but there is little or no literature to be found that solves these issues through the use of big data analytical tools and techniques. To fill this literature gap, this research is aimed at the determination of accurate criminal data from the huge mass of varieties of data using Hadoop clusters to support Social Justice Organizations in combating terrorist activities on a global scale. To achieve this goal, several algorithmic approaches, including parallelization, annotators and annotations, lemmatization, stop word Remover, term frequency and inverse document frequency, and singular value decomposition, were successfully implemented. The success of this work is empirically compared using the same hardware, software, and system configuration. Moreover, the efficacy of the experiment was tested with criminal data with respect to concepts and matching scores. Eventually, the experimental results showed that the proposed approach was able to expose criminal data with 100% accuracy, while matching of multiple criminal terms with documents had 80% accuracy; the performance of this method was also proved in multiple node clusters. Finally, the reported research creates new ways of thinking for security agencies in combating terrorism at global scale.