Brief: Cognitive system that analyzes data structures for meaning
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Related Systems: iaec sayer2

Code: GitHub

  • Sayer gets its name because it builds a context by asserting interesting facts about arbitrary Perl data structures. Sayer (along with thinker) is one of the most interesting projects of the FRDCSA. What it does is index arbitrary perl data structures, and attempt to derive interesting information and conclusions about that data using machine learning. For instance, if your data structure consisted of a string, and that string contained a paragraph of text, sayer would apply a decision tree or similar, set of tests, to determine that it indeed was as we described. It would represent this relation as a graph, with verticies as data-points, I.e. the input, and "true", and edges as function calls. All data of course is stored to a database. This graph data is then used as input to classifiers that attempt to distill summarily interesting information about said data. For instance, if it is a sentence, it may well wish to perform various NLP procedures, extracting things like named entities, and recursively analyzing those within it's attention span. It uses Perl as knowledge representation interlingua. The architecture is expansive, complex, and beautiful and integrates many other FRDCSA systems.