Analysing debates on climate change with textual entailment and ontologies

The difficult task of recognising textual entailment aims to check if a natural language text T entails a smaller statement H. Current methods rely on machine learning and various lexical resources. Our aim is to include domain knowledge when searching for entailment or non-entailment. As most available knowledge comes in form of ontologies, we focused on translating description logic axioms into lexical rules suitable for existing textual entailment algorithms. We apply the developed system in the climate change domain, where many pro and counter arguments do exist. The performed experiments indicate an increasing of performance when including domain knowledge into the existing textual entailment algorithms.