Granular Causality Applications: Using Part-of Relations for Discovering Causality

Causal markers, syntactic structures and connectives have been the sole identifying features for automatically extracting causal relations in natural language discourse. However, various connectives such as “and”, prepositions such as “as”, and other syntactic structures are highly ambiguous in nature, as they have multiple meanings besides causality. As a result, one cannot solely rely on lexico-syntactic markers for detection of causal phenomenon in discourse. This paper introduces the Theory of Granular Causality and describes a new approach to identify causality in natural language. Causality is often granular in nature (Mulkar-Mehta, 2011; Mazlack, 2004), and this property of causality is used to discover and infer the presence of causal relations in text. This is compared with causal relations identified using just causal markers. A precision of 0.91 and a recall of 0.79 is achieved using granularity for causal relation detection, as compared to a precision of 0.79 and a recall of 0.44 using text-based causal words for causality detection. Next, the author presents the findings for discovering causal relations between two sentences in an article. The system achieves a precision of 0.60 for discovering causality between two sentences using granular causality markers as features. The results are encouraging, and show that the granular causality is an important phenomenon in natural language