Examples for efficiently representing, processing and deciding amongst multiple ambiguous interpretations of human natural language text are described. Processing includes creating and augmenting an “interpretation graph” which represents all known ambiguous interpretations of some natural language text. The interpretation graph is made of vertices (junction points which lead to alternative interpretations) and ‘lexical items’ (natural language objects representing data blocks, tokens, word parts, phrases, clauses, parts of speech, entities, or semantic interpretations) that represent alternative ambiguous interpretations of portions of the text. The examples show a set of simple operations for augmenting the interpretation graph to create alternative interpretations. Finally, the method includes a notion of “confidence”, which is computed as the graph is being constructed and can be used by a selector once the graph is complete to choose the most likely interpretation followed by any number of increasingly less likely interpretations. By saving all known ambiguous or alternative interpretations in an interpretation graph, the example system can provide better accuracy, reliability and coverage since possible alternatives are not pruned until the final end-to-end interpretation is selected.