Multilayered Extended Semantic Networks as a Language for Meaning Representation in NLP Systems
CICLing '02 Proceedings of the Third International Conference on Computational Linguistics and Intelligent Text Processing
Automatic detection of causal relations for Question Answering
MultiSumQA '03 Proceedings of the ACL 2003 workshop on Multilingual summarization and question answering - Volume 12
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This paper presents a flexible retrieval method for Q/A systems based on causal knowledge. Causality is not only a matter of causal statements, but also of conditional sentences. In conditional statements, causality generally emerges from the entailment relationship between the antecedent and the consequence. In this article, we present a method of retrieving conditional and causal sentences, in particular those identified by the presence of certain interrogative particles. These sentences are pre-processed to obtain both single cause-effect structures and causal chains. The knowledge base used to provide automatic answers based on causal relations are some medical texts, adapted to the described process. Causal paths permit qualifications in terms of weighting the intensity of the cause or the strength of links connecting causes to effects. A formalism that combines degrees of truth and McCulloch-Pitts cells enables us to weight the effect with a value and thereby obtain a flexible answer.