Extraction and analysis of conditional and causal sentences for information retrieval
- Autores: Cristina Puente Águeda
- Directores de la Tesis: José Ángel Olivas Varela (dir. tes.)
- Lectura: En la Universidad Pontificia Comillas ( España ) en 2010
- Idioma: inglés
- Tribunal Calificador de la Tesis: Enrique Herrera Viedma (presid.), Asunción Paloma Cucala García (secret.), Alejandro Sobrino (voc.), Elie Sánchez Sánchez (voc.), Miguel Angel Sanz Bobi (voc.)
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- Resumen
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.
In this work the causal relationship is presented from a semantic point of view to establish a connection between antecedent and consequence in order to dispatch an inference process to obtain new causal relationships. This process may serve to provide deduction capabilities, very helpful in Information Retrieval systems such as search engines or Question Answering systems.
For this purpose, a computational 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. In the first one, an algorithm generates a causal knowledge base by means of automatic detection and classification processes which are able to extract those sentences matching any of the causal patterns selected for this task.
The second proposes an automatic mechanism which 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.
On the third step, a graphical representation of the causal relationships through a causal graph is generated, 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.
