Semantic Category Analysis

Semantic category theory indicates that human thinking involves four, entirely distinct types of information processing, each associated with a totally independent dasiasemantic categorypsila. However, observation also indicates that human cognition does not enforce strong data typing. Whilst this enables the creation of beautiful poetry and prose, weak data typing can also lie at the heart of persuasive but fallacious pronouncements, perplexing conundrums and puzzling paradoxes. Semantic category theory (SCT) is based on readily available observation and refutable by observation. SCT shows that when a characteristic associated with one Semantic Category has been assigned to a different semantic category, the resulting dasiasemantic intertwinepsila, underlies an often bewitching, sometimes bewildering, and inevitably erroneous, argument. Semantic category analysis (SCA), derived from SCT, enables any sentence, however complex, to be analysed in terms of its component semantic categories and their modes of combination. SCA thereby readily pinpoints any occurrence of Semantic Intertwine. SCA worked examples here include: resolving paradox, solving conundrums and clarifying philosophical questions, such as ldquoWhat is reality?rdquo SCA lends itself to automation and the possibility of Intelligent Machine communication in any domain of human discourse: however sophisticated.