Cognitive Causality Detection with Associative Memory in Textual Events

In previous research works, the causality generally refers to the existence of causality in mathematics and physics. In recent years, detection and clarification of cause-effect relationships, Causality Detection, among texts, events or objects has been elevated to a prominent research topic of natural and social sciences over the human knowledge development history. This paper demonstrates a causality detection algorithm (AM-CDA) inspired with cognitive associative memory. In the task of causality detection, AM-CDA implements a simple recurrent neural network (SRNN) to simulate human associative memory, which has the ability to associate different types of inputs when processing text information. The detection objects are simple sentences and clauses, which are treated as events in microstructure. AM-CDA has been fully examined in elaborately designed experimental tasks. The experimental results have testified the capability of AM-CDA in causality detection, and also indicate that further research works can improve the performance in standard evaluation measures.