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.

KSII Transactions on Internet and Information Systems TIIS Paper Details

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.