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Background. Automatic extraction of causal chains is valuable for discovering previously unknown and hidden connections between events. However, there is only a handful of works devoted to automatic extraction of causal chains from text. Objective. To develop a method for automatic extraction of causal chains from text. Method. A new approach based on linguistic templates is suggested for causal chain extraction. It is domain-independent, not restricted to extraction from single sentences and unfolded on big data. For implementation, a sequence of four modules was deployed. These are verb restriction, part-of-speech tagging, extracting causal relations, and unification and matching events. Results. 14,821 causal chains (with length=2) have been extracted from 100,000 English Wikipedia articles. Contributions. The extracted causal chains can contribute to developing commonsense knowledge bases, reasoning resources, problem-solving, and generally in discovering previously unknown relationships between entities/events.

Natural language communication between machines and humans are still constrained. The article addresses a gap in natural language understanding about actions, specifically that of understanding commands. We propose a new method for commonsense inference (grounding) of high-level natural language commands into specific action commands for further execution by a robotic system. The method allows to build a knowledge base that consists of a large set of commonsense inferences. The preliminary results have been presented.

Background. Automatic extraction of causal chains is valuable for discovering previously unknown and hidden connections between events. However, there is only a handful of works devoted to automatic extraction of causal chains from text. Objective. To develop a method for automatic extraction of causal chains from text. Method. A new approach based on linguistic templates is suggested for causal chain extraction. It is domain-independent, not restricted to extraction from single sentences and unfolded on big data. For implementation, a sequence of four modules was deployed. These are verb restriction, part-of-speech tagging, extracting causal relations, and unification and matching events. Results. 14,821 causal chains (with length=2) have been extracted from 100,000 English Wikipedia articles. Contributions. The extracted causal chains can contribute to developing commonsense knowledge bases, reasoning resources, problem-solving, and generally in discovering previously unknown relationships between entities/events.