1. A computer-implemented method for recognizing cause-effect relations in natural language documents to generate a cause-effect knowledge base comprising: recognizing a first expanded Subject-Action-Object (eSAO) set in a text from at least one natural language document using a processor, the first eSAO set including two or more eSAO components in the text grouped together to form a first semantic unit; recognizing a second eSAO set in the text from the at least one natural language document using the processor, the second eSAO set including two or more eSAO components in the text grouped together to form a second semantic unit, the two or more eSAO components grouped together to form the second semantic unit being different from the two or more eSAO components grouped together to form the first semantic unit; matching the first eSAO set and the second eSAO set against cause-effect (C-E) relationship patterns stored in a memory, wherein the C-E relationship patterns include indicators of a presence of a C-E relation in a sentence and information about an eSAO set in the sentence that acts as a cause part of the C-E relation and an eSAO set in the sentence that acts as an effect part of the C-E relation; generating one or more eSAO cause-effect relations based on the matching, wherein each eSAO cause-effect relation identifies one of the first eSAO set and the second eSAO set as a cause eSAO and the other of the first eSAO set and the second eSAO set as an effect eSAO; and storing the one or more eSAO cause-effect relations in a knowledge base.
1. A computer-implemented method for recognizing cause-effect relations in natural language documents to generate a cause-effect knowledge base comprising: recognizing a first expanded Subject-Action-Object (eSAO) set in a text from at least one natural language document using a processor, the first eSAO set including two or more eSAO components in the text grouped together to form a first semantic unit; recognizing a second eSAO set in the text from the at least one natural language document using the processor, the second eSAO set including two or more eSAO components in the text grouped together to form a second semantic unit, the two or more eSAO components grouped together to form the second semantic unit being different from the two or more eSAO components grouped together to form the first semantic unit; matching the first eSAO set and the second eSAO set against cause-effect (C-E) relationship patterns stored in a memory, wherein the C-E relationship patterns include indicators of a presence of a C-E relation in a sentence and information about an eSAO set in the sentence that acts as a cause part of the C-E relation and an eSAO set in the sentence that acts as an effect part of the C-E relation; generating one or more eSAO cause-effect relations based on the matching, wherein each eSAO cause-effect relation identifies one of the first eSAO set and the second eSAO set as a cause eSAO and the other of the first eSAO set and the second eSAO set as an effect eSAO; and storing the one or more eSAO cause-effect relations in a knowledge base. 8. The semantic processor of claim 1 , wherein the first eSAO set includes four or more eSAO components in the text grouped together to form the first semantic unit.