Complex Text Processing by the Temporal Context Machines

Abstract—It is largely unknown how the brain deals with time. Hidden Markov Model (HMM) has a probability based mecha-nism to deal with time warping, but no effective online method exists that can deal with general active temporal abstraction. By online, we mean that the agent must respond to spatial and temporal context immediately while a sensory stream flows in. By general active temporal context, we mean active (learned) attention selects desirable temporal subsets within a dynamic length of recent history (e.g., beyond bigrams and trigrams). By temporal abstraction, we mean using abstract meaning of context, supervised at the motor end, instead of iconic forms. This paper reports four experiments of complex text processing using the framework of a general-purpose developmental spatiotemporal agent called Temporal Context Machines (TCM), demonstrating its power of forming online, active, abstract, temporal contexts. We show that it perfectly (100%) solved a hypothetic problem called New Sentence Problem — after the TCM has learned synonyms under the corresponding contexts, it successfully recog-nized all possible new sentences (formed from the synonyms) that it has not learned. We show the TCM dealt with the Word Sense Disambiguation Problem where words are ambiguous without context. TCMs were also applied to the Part-of-Speech Problem, where the part of speech of the words in English language is identified according to contexts. In the fourth experiment, TCMs were employed to deal with the challenging Chunking Problem, in which subsequences of words are grouped and classified according to English linguistic units. I