The document discusses using automated semantic analysis to summarize and understand documents by treating them like machinery. It explains that semantic analysis can identify things that are broken, missing, or incorrectly assembled in a document by building a cognitive structure from the text. This allows the analysis to search and understand documents beyond the typical six pieces of information humans can handle at once.

1. Automated Semantic Analysis Doing something very hard but very useful Interactive Engineering Pty Ltd

2. 6 Pieces People can handle no more than six pieces of information in play at once – more than that and they chunk them (and crash them)

4. Self Extensible Active Structure We need to build a machine which uses an initial structure to read text, extends its structure as it does so, then uses that extended structure to read more of the text – the way a person does, but no program can

5. Its Knowledge Is Active Turning Circle “contains” the turn relation of the vehicle It looks like the FPS has a value for Turning Radius, when Turning Diameter was meant

7. Semantic Searching Sets propagate through the structure – the structure “works” A query structure is created using free text, and it is then matched to the structure built from the specification. These aren’t two different structures – the query structure is anchored in the structure built from the text to be searched, so common points are found during building of the query.

8. This Is Cognitive Using Semantics means doing everything differently – no short cuts, no “quick and dirty” analysis, no crummy programs, no pipelining into separate pieces – this is cognitive – it all happens together! The benefit – we transcend our human six pieces limit and extend our reach We make something of value in the world – and hope the world pays us for it