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Mining causality is essential to provide a diagnosis.This research aims at extracting the causality existing within multiple sentences or EDUs(Elementary Discourse Unit).The research emphasizes the use of causalily verbs because they make explicit in a certain way the consequent events of a cause,e.g.,“Aphids suck the sap from rice leaves. Then leaves will shrink.Later.they will become yellow and dry.”A verb can also be the causal-verb link between cause and effect within EDU(s),e.g.,“Aphids suck the sap from rice leaves causing leaves to be shrunk”(“causing”) is equivalent to a causal-verb link in Thai).The research confronts two main problems:identifying the interesting causality events from documents and identifying their boundaries.Then,we propose mining on verbs by using two different machine learning techniques,Naive Bayes classifier and Support Vector Machine.The resulted mining rules will be used for the identification and the causality extraction of the multiple EDUs from text.Our multiple EDUs extraction shows 0.88 precision with 0.75 recall from Naive Bayes classifier and 0.89 precision with 0.76 recall from Support Vector Machine.
Mining causality is essential to provide a diagnosis. This research aims at extracting the causality existing within multiple sentences or EDUs (Elementary Discourse Unit). The research emphasizes the use of causalily verbs because they make explicit in a certain way the consequent events of a cause , “Aphids suck the sap from rice leaves. Then leaves will shrink. Fluid. the will be yellow and dry.” “A verb can also be the causal-verb link between cause and effect within EDU (s), eg , ”Aphids suck the sap from rice leaves causing leaves to be shrunk“ ( ”causing ") is equivalent to a causal-verb link in Thai). The research confronts two main problems: identifying the interesting causality events from documents and identifying their boundaries. Chen, we propose mining on verbs by using two different machine learning techniques, Naive Bayes classifier and Support Vector Machine. The resulting mining rules will be used for the identification and the causality extraction of the multiple EDUs fro. m multiple. Our multiple EDUs extraction shows 0.88 precision with 0.75 recall from Naive Bayes classifier and 0.89 precision with 0.76 recall from Support Vector Machine.