In a Nutshell

Introducing terms: Word-Topic & Quadranym

Word-Topics are about the dynamic sense of a word or word sense.

Word-Topics (or micro-topics); help organize, characterize or summarize lexical information. They’re pre-textual micro-unit renderings of context used to anchor focus given to a sentence and to contextual development.

Every Word-Topic has four basic dimensions called a Quadranym.

Example: Time(x)

  • x: {future, past, present, event}
  • modes: {future, past}
  • states: {present, event}

Reference frame:

  • zero point = present
  • coordinates = events
  • y axis = future
  • x axis = past

A reference frame is the framework used to organize a word’s related text. In text analysis Word-Topics organize from general structures to specific structures forming hierarchical layers. The text is parsed into these layers.

Humans are highly creative with context. That is, contextual tricks to imagine possibilities. It’s a key factor to how we deal with uncertainty. Consider the ‘survey question’ below for what is x. Like in the game of Family Feud, the most common answer to the question wins the round.

  • “I will know x as soon as I walk through the door.”

Give a person a situation like this and countless scenarios are imagined. It’s virtually impossible to know what the speaker is referring to. Still, the job of commonsense prediction is about the knowledge of what typically happens; “what the weather is like,”“what’s for dinner,” “If the package arrived.”  We refer to this knowledge as the Situational Context.

  • In the Q approach, a prediction is one thing, a response is another.

We introduce the idea of the Dynamical Context. It is essentially a point of view and is represented in Quadranyms (below). The responsiveness of quadranyms to conditions are virtual adaptations to the environment. Consider different responses to the condition dynamic of x.

  • x = “I will know as soon as I walk through the door.”
Quadranym Examples

Topic Name: Agent(x)

For all x, If  x is agent Then, x is:

  • Mode Sets: Expand = active ⊇ Reduce = passive
  • State Sets: Subject = self ⊇ Object = goal

Topic Name: Space(x)

For all x, If  x is space, Then x is:

  • Mode Sets: Expand = infinite ⊇ Reduce = finite
  • State Sets:  Subject = void  ⊇ Object = between

Topic Name: Time(x)

For all x, If x is time, Then x is:

  • Mode Sets: Expand = future ⊇ Reduce = past
  • State Sets: Subject = present ⊇ Object = event

Topic Name: Mental(x)

For all x, If  x is mental, Then x is:

  • Mode Sets: Expand = unknown ⊇ Reduce = known
  • State Sets: Subject = knower ⊇ Object = knowable

For all x, If  x is locomotion Then, x is:

  • Mode Sets: Expand = move ⊇ Reduce = stay
  • State Sets: Subject = position ⊇ Object = place

Topic Name: Door(x)

For all x, If  x is door Then, x is:

  • Mode Sets: Expand = open ⊇ Reduce = close
  • State Sets: Subject = passage ⊇ Object = barrier


  • (x)  door(x) [Open(passage) Close(barrier)(x)]

Virtual Perspective: Each quadranym represents a discrete dynamical system. All systems draw upon the environment and then (in response) give back to it, thus participating in an ecology of dynamical systems.

Word-sensibility is about words operating like units of homeostasis. A word is like a sensing unit of homeostasis, where it responds to the changes monitored in a text. The balance is between expansive and reductive modes on an x-y axes positioned by coordinates of a zeropoint.

To summarize, the basic idea is for machines to have the ability to learn to abstract the human experience of contextual expectation, building that abstraction up from the word-level, and in this way machines can make better sense of the things that people typically know. People experience the world and computers don’t. Essentially, this is why commonsense is a challenge for computers. Computers can’t possibly know all the things that people experience and know. However, by being able to improve contextual expectation a machine can better learn from its mistakes. The goal is textual analysis to assess word sense by keying on human responsiveness. That is, the ability to act quickly and positively to situations in the world.



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