Retrieving crisp and imperfect causal sentences in texts

from single causal sentences to mechanisms

Cristina Puente

Alejandro Sobrino

José A. Olivas

pp. 175-194

in: Rudolf Seising, Veronica Sanz González (eds), Soft computing in humanities and social sciences, Berlin, Springer, 2012

Abstract

Causality is a fundamental notion in every field of science. In empirical sciences, such as physics, causality is a typical way of generating knowledge and providing explanations. Usually, causation is a kind of relationship between two entities: cause and effect. The cause provokes an effect, and the effect is derived from the cause, so there is a relationship of strong dependence between cause and effect. Causality and conditionality are closely related. One of the main topics in the field of causality is to analyze the relationship between causality and conditionality, and to determine which causal relationships can be formulated as conditional links. In this work a method has been developed to extract causal and conditional sentences from texts belonging to different genres or disciplines, using them as a database to study imperfect causality and to explore the causal relationships of a given concept by means of a causal graph. The process is divided into three major parts. The first part creates a causal knowledge base by means of a detection and classification processes which are able to extract those sentences matching any of the causal patterns selected for this task. The second part selects those sentences related to an input concept and creates a brief summary of them, retrieving the concepts involved in the causal relationship such as the cause and effect nodes, its modifiers, linguistic edges and the type of causal relationship. The third part presents a graphical representation of the causal relationships through a causal graph, with nodes and relationships labelled with linguistic hedges that denote the intensity with which the causes or effects happen. This procedure should help to explore the role of causality in different areas such as medicine, biology, social sciences and engineering.