Team leader training applications often include critical decision making based on multiple streams of communications where an overload, or interference from secondary sources, can occur. Artificial intelligence based parsing of language could aid in the presentation of the information contained in communications. Semantic and pragmatic context of a group of messages can be restricted in terms of specific domain information relating to events. Further, rule based software written in programmable logic can model the rules of the domain phraseology.

Computational linguistics makes use of syntactic parsing whereby a stream of symbols is analyzed for its conformance to some specific set of rules. For instance, if you read the previous sentence, you "syntactically parsed" it according to some grammar of "English" and determined that it was grammatical. In the simplest terms, parsers analyze some target string of symbols according to some specified grammar.

In a probabilistic associative chain, the occurrence of each word is determined by the immediately preceding word or series of words, where each response serves as a stimulus for the next response. In bounded phraseologies such as those used in training devices, it is possible, using programmable logic, to model grammars. Similarly, the association of words in very large grammars can be modeled statistically. Two innovative computational linguistic approaches were investigated and presented in this paper, the cross entropy syntactic identifier and the Markov model syntactic parser. The cross entropy syntactic identifier identifies phonetic similarity and the Markov model syntactic parser adds variability to otherwise rigid parsing techniques.