System Page

Introduction

Page Summary: Commonsense knowledge is widely considered to be one of the most difficult issues in AI. In this theoretical approach, we apply a paradigmatic relationship layer to a lexicon in which the data components are related to form micro-topics. The product resembles a thesaurus. It is a schema (and acquisition) relaying micro-topics to natural language programs that aim to analyze text requiring high levels of commonsense reasoning. The schema doesn’t necessarily provide the reasoning. Rather, it provides orientational gadgets called, motivated-dynamical-contexts. In this article, we introduce the gadgets, context systems, knowledgebase and schema. The project includes a wiki, source code, various acquisitions & interfaces. A Word-Sensibility Model is introduced as a concept basic to the project.

The Quadranym Model of Word-Sensibility (Q): An Ecological Systems Perspective On Word-Level Concepts & Contextual Unitizations – Non-Mental-Representation Representation Design Before Define Approach.

The application aims to assist access to data in dictionaries as pertaining to commonsense situations in text; a method concerned with computational lexicography and ontology for AI. It involves a meta-lexicography as it aims to deal with accessing word sense using lexical paradigms to search words and simulate the human ability to sense dynamic relations between signs.

In the Q, the signifier requires two different systems of context to be signified. Q analysis makes a distinction between the situational context (world conditions) and the dynamical context (organismic responsiveness). The Q is about an ecological system of agency for accessing knowledge.

The project is about developing an abstract representation for how human responsiveness responds to written text. Some inspiration comes from the concept of experiential traces as found in the embodied language processing hypothesis. The goal is textual analysis to assess word sense by keying on human responsiveness (i.e., the ability to act quickly and positively to situations in the world). It is about commonsense grounding and metaphoric structure. Ideally, it aims to help assess dynamic word relations of the type found in mythic stories. Our current goal is to build an ontology and API pertaining to a lexical layer of word-sensibility that sits between a lexicon like WordNet and a knowledge graph like ConceptNet. Central to the idea is intersubjectivity, specifically how people share orientation and focus and how this dynamic can be extended to machines.


The Dynamical Context

Unpacking words with Q analysis begins with a distinction in the contextualizing of word sense. A Situational Context is the communicative ability to present or understand the objective circumstances in which an event occurs and will sometimes include the appropriate behaviors associated with it. We introduce the idea of a Dynamical Context which is something different and can be summarized as follows:

  • Dynamical Context: a situation resonates with a preexisting psychology, a predetermined expectation for behavior within that situation, and produces a synergy response, reshaped for the moment.
  • Dynamical Contextual Systems: characterized by the potential for multiple dynamic areas and interactions between them.

We introduce conceptual constructs called quadranyms and polynyms. The terms when pertaining to system processes form a matrix that represents a general and overarching cognitive framework for a Dynamical Contextual approach for an ontology of commonsense. Humans are wired to connect. The focus is on the dynamicity of a word’s specificity to relate word sense.


 Quadranyms & Polynyms

We introduce a method to acquire sets of conceptual relations in a format of dimensions. We call it nymology, a practice of collecting and organizing idea sets: each a set of terms representing a certain number of dimensions used to strategically unfold, frame or simplify a concept. Each set functions as either part, step or type. A dimension number is called a polynym i.e., mononym, duonym, trionym, tetranym, pentanym and so on. Entries include, area, source, URL and logged by. A collection resembles a thesaurus and can be used to help populate knowledgebase systems and enhance queries.

What is a Polynym?
We use Polynym to describe idea sets (as part, step or type), such as:

  • 3 parts × Freud’s psyche = [ Id, Ego, Superego ]
  • 7 types × Deadly sins = [ Wrath, Greed, Sloth, Pride, Lust, Envy, Gluttony ]
  • 5 steps × Grief = [ denial, anger, bargaining, depression, acceptance ]

There is a difference between artifactual polynyms based on long term study (accepted or rejected) and system processes representing polynym constructs assembled for the moment, to contextualize a situation.

  • Polynym idea sets are strategic ways to think about a topic. Strategies pertain to the Situational Context. That is, what is true or false. It is a  deliberative process in that a topic is about what the condition is.
  • Polynym idea sets exist in virtually every discipline and are abundant. Polynyms are also important to a system’s basic process. They are about the truth conditions of the real world, including attitudes and behaviors.

What is a Quadranym?
We define Quadranym as a four-part conceptual construct using a dual-axis Mode-State model. It operates as a virtual unit of orientation & constraint.

Expansion-Reduction (ER-mode) and Objective-Subjective (OS-state).

  • Quadranym idea sets are about framing a response to a topic. Responses pertain to the Dynamical Context. That is, how one copes. It is on the heuristic level in that a topic is about how to respond to a condition.
E = expand: open
O = object: barrier
N = Topic Name: door
S = subject: passage
R = reduce: close

A responsiveness for door is rendered, a dynamic framework for specified terms; a constraining unit specifying a virtual motivated-dynamical-context.

The quadranym (word-topic) model is inspired by embodied language processing involving motor and sensory perception and experiential traces.

Word-Topics (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.

Quadranym Representation Introduction:

The quadranym square divides into left and right hemispheres, top and bottom levels and diagonally related on mode & state axes.

Slide1

The general configuration is as hemispheres and is represented as follows:

  • Right Side (Superset): Active_Potential(active_actual)
  • Left Side (Subset): Passive_Actual(passive_potential).
  • Superset = Active Sense: more experience necessary (find potential)
  • Subset = Passive Sense: no more experience necessary (potential ready)

Active sense initiates a micro-topics function and argument. Passive sense is when the function and argument is finally configured for the situation. Actual state is the source. Potential state is the target. For instance, in the next example, the active sense of space is void. The passive sense of void is between. Between is whatever the situation is. If it is the walls of a room or a passage between rooms, then it is those specific things. Void (or emptiness) remains active no matter what the situation is. The active sense is the question, the passive sense is the answer. The quadranym does not necessarily give the answers, rather it aligns its active sense with the text it is analyzing to make sense of the situation as a potential of the real world.

The superset is the coherent condition of the set. The subset is the potential target set. Terms cluster between the sets as follows:

  • The superset is the source condition.
  • The subset is the target variable.

Slide2

Each dimension holds its own categorical set. For example, open holds over, inclusive, infinite, out and all as some factors that unpack given the context.

Cluster: {…}

  • Open{…)(passage{…}) ⊇ Close{…}(Barrier{…})

Consider the utility of door as described in the following situation…

Situational  Context

Text: “Close the door because it’s cold outside.”

Dynamical Context

Topic Factors: {time, space, door, valence, temperature}

Inference: from, now to in to space to open to door to close to temp to affect.

Future{…)(present{…}) ⊇ Past{…}(event{…})

Infinite{…)(void{…}) ⊇ Finite{…}(between{…})

Open{…)(passage{…}) ⊇ Close{…}(barrier{…})

Cold{…)(optimal{…}) ⊇ Warm{…}(condition{…})

Positive{…)(affect{…}) ⊇ Negative{…}(situation{…})

The example shows how quadranyms can configure statistical models. In this realm, door is configured to assess optimization in a climate context.

door

100% closed is better than 90% closed. 90% closed is better than 50% closed. Temporal and spatial factors configure together to optimize desired conditions; procedure = less open & less time open. If door remains open desired temperature will be reduced. Because door is a subset of the spatial domain it is nested in spatial dimensions. This form is illustrated in any matrix table of nested topics. The point being, the variability in climate is dependent on the division of spatial modes, all_out & some_in. All space is out temperature non-discrete. In the door realm, any variance is dependent on barrier, i.e.,  close_in is the independent variable that influences open_out temperature. All Quadranyms can conform in this way in some manner.


 Micro-Topics

Quadranyms are less about word relations and more about topic factors. In our approach, 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 know. However, by being able to improve contextual responsiveness a machine can better learn from its mistakes.

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.

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

We introduce the idea of the dynamical context, represented in Q-units. The responsiveness of units to conditions are virtual adaptations to the environment. Consider different responses to the condition dynamic x.

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

Topic Name:Space(x)

For all x, If  x is space, Then x is:

  • Mode Sets: E = infinite ⊇ R = finite
  • State Sets:  S = void  ⊇ O = between

Topic Name:Time(x)

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

  • Mode Sets: E = future ⊇ R = past
  • State Sets: S = present ⊇ O = event

Topic Name:Mental(x)

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

  • Mode Sets: E = unknown ⊇ R = known
  • State Sets: S = knower ⊇ O = knowable

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

  • Mode Sets: E = move ⊇ R = stay
  • State Sets: S = self ⊇ O = place

Topic Name:Door(x)

For all x, If  x is door Then, x is:

  • Mode Sets: E = open ⊇ R = close
  • State Sets: S = passage ⊇ O = barrier

Shorthand:

  • (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.


 Quadranym & Polynym Systems

Summary: Polynyms and Quadranyms are fashioned by people or can be generated by a system to represent strategic ways of thinking or sense-making abilities. Our hope is that once quadranyms and polynyms are better understood their practical and diverse applicability will be apparent.

Below is a Q table. It represents a Polynym. Each topic name represents one of its dimensions (ranks). Polynym dimensions can be of any number.

This one is fashioned in five dimensions (i.e., pentanym).

5 parts × Relations of Locations = [space, door, distance, direction, container]

Topic Name Expansion Reduction Objective Subjective
space infinite finite between void
door open close barrier passage
distance far near relation position
direction there here to from
container out in full empty
time
future past event present

Unlike polynyms that have any number of dimensions, quadranyms align and nest (local) topics using four (global) dimensions. These dimensions form all quadranym units. The terms in each of the (EROS) columns tend to fill in a tantamounting or pervading sense. Meta-Dimensional Roles are the general terms (M-roles). Specific roles are called content roles (C-roles). C-roles are attracted to M-role states. M-roles work to make C-role relations tractable.

In the matrix above, notice how temporal & spatial realms align or nest together. Although the nestings suggest certain relationships, these are primary positions that can be altered through scripts as we will see later.

Quadranyms are pre-textual anchors making motivated-dynamical-contexts out of words before any textual influence then, reshapes for the situation.

Q analysis is about how dynamic roles are equivocated to satisfy the task of describing a condition. Roles of a column can be connected using relations.

  • Conceptnet types, e.g., between isA barrier, open motivatedBygoal out.
  • Also, relations can cross rank topic terms, e.g., out obstructedBy barrier.

A quadranym is a simple organizing construct that is reasonably explained in minutes. Below is an introductory outline of the quadranym dimensions.

The Prime Dimensions & M-Role Examples:

  • E: Expansive (potential mode) term examples: potential, active, unity, group, over, all, new, implicit, big, learning, playing (or a kind of child like view of life where the world is constantly unfolding), mystery, sustain, spatial openness.
  • R: Reductive (actual mode) term examples: actual, passive, plurality, it, that, you , me, fact, in, down, measure, explicit, focus, small, (or a kind of adult view of life where the world needs controlling), familiar, deny, spatial closeness.
  • O: Objective (potential state) term examples: potential, becoming, condition, variant, practice, decide between, interpersonal, social, temporal endings.
  • S: Subjective (actual state) term examples: actual, being, coherent, core, constant, perspective, beliefs, desires, intrapersonal, temporal beginnings.

Various metaphysical notions apply to the quadranym prime dimensions, such as, part/whole, plurality/unity, being/becoming, time and space.

Note that actual-state refers to a unit’s actual state of being. This mainly refers to the context of being, core sense and self-centered bias. When referring to physical properties & laws, actual is a situational context. We call this the Objective Field, i.e., real-world entities. A dynamical contextual orientation virtually seeks to optimize its relations to the objective field.

  • Actual-State of the Dynamical Context = self-centered world or umwelt.

Q words are written, nTopic = Topic Name for Word. For example…

  • nSpace
  • nDoor
  • nDistance
  • nDirection
  • nContainer

Quadranyms act like anchors of word-sensibility for word sense.

Consider space and its word sense entries from WordNet.

Noun

  • S: (n) space, infinite (the unlimited expanse in which everything is located) “they tested his ability to locate objects in space”; “the boundless regions of the infinite”
  • S: (n) space (an empty area (usually bounded in some way between things)) “the architect left space in front of the building”; “they stopped at an open space in the jungle”; “the space between his teeth”
  • S: (n) space (an area reserved for some particular purpose) “the laboratory’s floor space”
  • S: (n) outer space, space (any location outside the Earth’s atmosphere) “the astronauts walked in outer space without a tether”; “the first major milestone in space exploration was in 1957, when the USSR’s Sputnik 1 orbited the Earth”
  • S: (n) space, blank (a blank character used to separate successive words in writing or printing) “he said the space is the most important character in the alphabet”
  • S: (n) distance, space (the interval between two times) “the distance from birth to death”; “it all happened in the space of 10 minutes”
  • S: (n) space, blank space, place (a blank area) “write your name in the space provided”
  • S: (n) space (one of the areas between or below or above the lines of a musical staff) “the spaces are the notes F-A-C-E”
  • S: (n) quad, space ((printing) a block of type without a raised letter; used for spacing between words or sentences)

A lexicon like wordnet defines a word (with gloss and synsets) for as many distinct senses in the entry. A word’s general message or pervading sense is, in some manner, often perceived in each of the different senses. What kind of framework can be used for this kind of analysis? Researchers are discovering that the mind responds to written text using many of the same resources (i.e., motor and sensory perception) used to interact in the world. Regarding words only as atomic sentential elements of grammar does not suit this kind of analysis. The grounded sense of words seem to exist in the interactions between us and those things we resonate with in the world; each word, remembered dynamics, educing a sense of, dynamical orientation.

  • Orientational Template: nTopic[E_mode(s_state) ⊇ R_mode(o_state)]
  • A topical orientation of space: nSpace[Infinite(void) ⊇ Finite(between)].
  1. Mode Sets: potential ⊇ actual (a.k.a. predicates & action)
  2. State Sets: actual ⊇ potential (a.k.a. subjects & being)

Quadranyms contain two sets: the superset virtually holds all the elements that the system has on nTopic. This is the source set (a.k.a. the unattended set). Any subset of the source is the target set (a.k.a. the attended set).

  • unattended_source ⊇ attended_target

Quantifying: all or some default variables: Infinite = All Finite = Some. Dynamical Context seeks to attach to Situational Context, e.g., The tiny room.

  • Room(x) → [Infinite(void_tiny) Finite(between_walls)(x)]

Tiny is anchored to the subject void predicated on the potential, infinite. Infinite is the dependent variable, finite is the independent variable. The dynamical context is orientation dependent on the independent situation.

Below is a list of synonyms (C-roles) for space from Thesaurus.com.

Space {area, arena, capacity, distance, field, location, slot, spot, territory, zone, amplitude, blank, breadth, compass, expanse, expansion, extension, extent, gap, headroom, headway, infinity, interval, lacuna, leeway, margin, omission, play, range, reach, spaciousness, sphere, spread, stretch, tract, turf, volume, elbowroom}.

  • Create trajectories for: source set ⊇ target set.

For the space template; void anchors the dynamics of the entire set. It functions like a heuristic bias and is referred to as the Coherent Bias. All C-roles are anchored on void and become attended under the term between.

  • M-roles: {<void, between>}
  • C-roles: {area, arena, capacity, distance, field, location, slot …}

Q Gloss Utility: If/Then Template for C-Roles Intending Space:

  1. IF void IS area THEN it IS between xhere AND xthere.
  2. IF void IS arena THEN it IS between xthis AND xthat.
  3. IF void IS capacity THEN it IS between xempty AND xfull.
  4. IF void IS distance THEN it IS between xhere AND xthere.
  5. IF void IS field THEN it IS between xthis AND xthat.
  6. IF void IS location THEN it IS between xthis AND xthat.
  7. IF void IS slot THEN it IS between xthis AND xthat.
  8. and so on…

Consider, distance:

  •  Potential relation from one’s position, far and near are the actions.

Below, a list of synonyms (C-roles) for Distance from Thesaurus.com .

Distance {area, length, orbit, radius, scope, separation, size, space, span, stretch, width, absence, ambit, amplitude, bit, breadth, compass, expanse, extension, extent, farness, heavens, hinterland, horizon, lapse, objective, outpost, outskirts, provinces, purlieu, purview, reach, remoteness, remove, sky, spread, sweep, way, country mile}

All of the C-roles are anchored on position and attended under relation.

  • M-roles: states:{<position, relation>}
  • C-roles: targets:{area, length, orbit, radius, scope, separation, size …}

Q Gloss Utility: If/Then Template for C-Roles Intending Distance:

  1. IF position IS area THEN relation IS xhere AND xthere.
  2. IF position IS length THEN relation IS FROM xthere TO xhere
  3. IF position IS orbit THEN relation IS xcentral TO xhere
  4. IF position IS radius THEN relation IS xcentral TO xhere.
  5. IF position IS scope THEN relation IS FROM xthis TO xthat.
  6. IF position IS separation THEN relation IS xthere NOT xhere.
  7. IF position IS size THEN relation IS FROM xbig TO xsmall.
  8. and so on…

The model aims to assist in the disambiguation and predicting of word use. However, its prime function is to retrieve the dynamic senses of a message.

Q analysis is based on the Dynamical Context and not the Situational Context. The differences between contexts is briefly reviewed in the introduction. Next, is an analysis of a basic Dynamical Context Script.

Orientational Analysis: Topic Name; distance:

The true meaning of distance is found in the situational context. That is, the situational-context text answers for the dynamical-context target variables.

  •  “My friend lives far from here.”

Textual Elements: {my, friend, live, far, from, here}

Cued Terms (elements) Form a Pentanym:

  1. Mine(x) → [Give(possess) ⊇ Keep(object)](x)
  2. Friend(x) → [Affection(self) ⊇ Genial(companion)](x)
  3. Reside(x) → [Move(live) ⊇ Stay(visit)](x)
  4. Distance(x) → [Far(position) ⊇ Near(relation)](x)
  5. Direction(x) → [There(from) ⊇ Here(to)](x)
Topic Name Expansive Reductive Objective Subjective
mine give keep object possess
friend affection genial companion self
reside move stay visit live
distance far near relation position
direction there here to from
container
out in full empty

Polynym ranks represent contextual timelines. That is, hierarchical layers form with the textual development and topic rankings change in real time.

Brief Description of Contextual Timeline:

Quadranyms form units. Units are frames. When linked together form scripts. Polynyms refer to each layer of script. Scripts run simultaneously on different contextual timelines. Upper scripts constrain lower scripts.

  • This refers to procedures e.g., when you get up for work; take shower, brush teeth, have breakfast, rush out etc… work forms the contextual timelines.

Details are beyond the scope of this article. More information: Model Page

The Script Example:

Before we analyze the script, we will first review its basic concept. The idea is to have a handful of general concepts per domain to model scripts on.

  • Scripts are reused and modified for different domains and situations.

The Game of Proximity & Remoteness:

Consider the table above, there are certain relationships that will be relevant to the text and easily applied to the script. In this analysis, we focus on the metaphysical paradigm on which this script functions. It deals with the organism’s central position to remoteness & proximity. A lot of moving parts—difficult to explain but easy for a computer to execute.

Below are two different senses of distance. Each plays a part in how the paradigm of the script plays out; distance 1 conforms to distance 2.

  1. Distance → [Far(position) ⊇ Near(relation)].
  2. Distance → [Remote(central) ⊇ Proximity(location)].

Spatial sense virtually plays out in this script, an analog for clustering and separating of what is inclusive or exclusive; a spatial metaphor concerned with organizing thoughts in terms of relations of locations.

The Two Unit’s Pair Relations (by columns):

  • <central _ position>, <remote _ far>, <location _ relation>, <proximity _ near>.

Each pair has a relation variable for: partOf, stepTo, typeFor.

Although, a conclusion and an interpretation will be given by the script, concluding is not its primary function, which is to cluster words into sets.

  • Goal: find optimal path from dynamical context to situational context.

M-role_Subject Cache for Script Distance(x):

  • Cache of Probing Terms:{position, relation, object, place}

Mixing in the Mixer:

Subject terms & relations are weighted & sorted into the script, including, polynym table equivocations, M-roles, C-roles, sources, targets, root relations.

  • Prep includes, knowledge graphs, word vectors & data training.

Scripts represent a general model of sequential learning. A script cycles and closes the loop on role relations. State M-roles iterate as terms are sorted into their C-Roles i.e., textual terms are mixed in with topical & root terms. All textual terms become subjects of the script. The grammar is set aside.

  • The sequence of a script represents an interactive procedure for learning.

Script:

Gross Units:

  • [Far(position) _ Near(relation)]<find>[Far(relation) _ Near(object)]<find>[Far(object) _ Near(place)]<find>[Far(place) _ Near(object)]

(<find> refers to find pair relations. Underscore ( _ ) represents relation variables)

Net Units:

  • Far(relation) + Far(object) + Far(place) = Near(object)

Script Analysis:

Text: “My friend lives far from here.”

Premise: Distance → [Far(position) ⊇ Near(relation)].

Template & Color Key:

  1. Mode Sets: potentialactual (i.e., predicates of paradigm)
  2. State Sets: actualpotential (i.e., subjects of paradigm)
  • The paradigm begins at the zero-point. This represents the actual-subject i.e., the umwelt as a deictic center. It is predicated on potential. It targets what is predicated on actual. Any target is the potential-subject of any situation.
  • Target M-role of previous unit becomes Source M-role of next unit.
  • Default Social Relation: source_self → target_other

Gloss Utility Layer & Subject Primer: 

1, Position: IF position IS position THEN relation IS here OR there.

  • Paradigm: [Far_there(position_here) ⊇ Near_here(relation_there)]

2, Relation: IF position IS relation THEN object IS this_self AND that_other.

  • Paradigm: [Far_there(relation_self) ⊇ Near_here(object_other)]

3, Object: IF position IS object THEN place IS here_near OR there_far.

  • Paradigm: [Far_there(object_near) ⊇ Near_here(place_far)]

4, Place: IF position IS place THEN object IS here_self NOT there_other

  • Paradigm: [Far_there(place_self) ⊇ Near_here(object_other)]

Textual Elements Result Layer:

  1. position {self_my_from_here} <find> relation {other…}
  2. relation {other} <find> object {person_friend}
  3. object {friend} <find> place {far}
  4. place {far} <find> object {lives_friend}
  • Green set terms are related by partOf.
  • Orange set terms are related by stepTo.
  • Root terms are related by typeFor.

Predicate Layer: Mode sense assertions virtually oscillate in script.

First Unit:[Far(position_self_my) ⊇ Near(relation_other_object)]

  • Potential_Far(x) asserts a continuous-out i.e., global.
  • Actual_Near(x) asserts a discrete-situation i.e., local.

Second Unit:[Far(relation_there_from) ⊇ Near(object_friend_to)]

  • Far is a continuous variant i.e., inclusive position.
  • Near is a discrete invariant i.e., exclusive position.

Third Unit:[Far(object_there_out) ⊇ Near(place_here_in]

  • How is textual subject “far_friend” when it is predicated on Near?
  • Near & Far are paradoxically conflicted. Only near is quantifiable.

Forth Unit:[Far(place_there_lives) ⊇ Near(object_friend_stays)]

  • Far(x) = Remote(x) when the preferred term* anchors the target.
  • The target_friend anchored on source_place completes script.

*(Best sense anchors unit, e.g., place anchors object = discrete final unit.)

Conclusion: Far(relation) + Far(object) + Far(place) = Near(friend).

Script Interpretation:

Discrete = []

  • IF:
    [Far_relation] = [Far_friend]
    THEN
    [Far_place = Near_friend]
  1. Potential_Far = continuous variant, i.e., unity (e.g., central).
  2. Actual_Near = discrete invariant, i.e., individual (e.g., proximity). 
  3. Production Units = discrete variants, i.e., plurality (e.g., remoteness).

(Production units, procedural units or sub-anchors are different terms used to think about the same thing; units constrained by an initial orientational unit.)

Virtual orientation shared between speaker & hearer:

  • Distance → [Remote(central) ⊇ Proximity(location)]

[ Actual Roles = central proximity Potential Roles =  remote location ] 

slide1

Key Idea: The situational context (text) is like an independent variable that influences the dynamical context or, as it were, the dependent variable.

Subject Layer & Dynamic Relations:

Relations won’t be covered here. Briefly for now, consider additional Topic Candidates: {mine, direction, container, reside}. Textual terms are sorted into topic lists and related. M-roles virtually respond to situation.

  • reside → Move(live) _ Stay(visit)
  • distance → Far(position) _ Near(relation)

Pairings can be rated as strong or weak, e.g., live partOf position = strong sense in the text while visit stepTo relation = weak in the text.

Q Relations: 3 Bidirectional (↔︎ ) Types:

  • partOf: terms related by unity (i.e., belonging to an orientation).
  • stepTo: terms related by sequence (i.e., procedure of orientation).
  • typeFor: terms related by purpose (i.e., this is used for that).

Also: typeOf: terms related by features.

Given the dynamical context as exemplified in the text, live is partOf position in the relation of reside and distance. Consider other relations.

  1. live partOf position
  2. move stepTO position
  3. place typeFor position

Review: Game of Proximity & Remoteness

The situational context “far” shapes the dynamical context. Because of “far friend”, the initial_Far is enacted; an asserted-potential continues outward. Because proximity is actual in each unit, the initial-anchor’s potential Far constrains each discrete unit’s potential, until the actual subject is found.

  • Found = Distance:[Far(place) → Near(object)]

The actualness of the textual situation is found in the discreteness of this anchor. It is the final unit that brings remoteness to the script. Potential(x) belongs to the initial unit while Actual(x) belongs to the final unit.

Review Script Actions & Components:

  • super-anchor → sub-anchor.

Position is the prime anchor, relation is the prime target. As the script progresses relation becomes a sub-anchor. Other terms become targets and then sub-anchors. This continues with textual terms as they get mixed in.

  • Superset = Active Sense: more experience necessary (find)
  • Subset = Passive Sense: no more experience necessary (found)

Conditional Quadranym:

  • Conditional →  If(subject) ⊇ Then(requirements)

Social Realm Root Relation: <self_other_entity_object_person>

  • Social & physical default = source_ self → target_other.

Note; The root relation begins on self, i.e., self centered world or umwelt. The idea is that the central self targets its own interactive identity with the world.

Review System Selections:

Not all textual units need to run as scripts. Distance is a reasonable general orientation, given the text, for Q-roles of other topics to be scripted into.

  • Find optimal quadranym for general constraint; “far_object” = distance

The anchor (distance) may continue into paragraphical development and beyond. Every quadranym can re-scale, from word-topic to theme-topic.

  • Constraints nest levels of the dynamical context to fit textual situations.

Scripts are not about fact finding although they assist in that. They are about the subjects that offer possible new relevancies based on the text.

  • Equivocations are opportunities for new & relevant explanatory relations.

Synopsis:

Quadranyms represent the smallest unit of context in the system. Although they are rendered to effectually deal with word sense they are scalable units able to span from word-topics to theme-topics. They are about virtual states of sensibility and represent a responsiveness toward something turned contextual artifact. That is, an orienting context of perception that is dynamically actual and situationally potential, a dynamical context.

Every unit is a trajectory. The trajectory is about what is being and how that being may become. That is, each subjective state (being) has an objective state (becoming) to identify. Each is predicated by modes (e.g., distance: far & near). Each unit initiates from the actual state (superset subject) and is always predicated on the expansive mode category. This reflects a basic principle of the word-sensibility model, that what is actual is the response being sensed. Logic is a form of reasoning used to reach a conclusion using the most accurate facts available. This is not how commonsense necessarily functions as it can manifest on social assumptions and no facts. The model aims to abstract how assumptions form. Commonsense reasoning does not always strictly adhere to logic as it sometimes aims to make points by generating and using rhetoric to form arguments. The Q explores how a system does this (i.e., form & share novel ideas). Topical interoperability is the ability to accept or reject the orientations or conclusions of other systems.

Basically, a script allows a target of some condition to become the new bias. Q is about heuristic thinking or quick thinking. Slower more deliberative thinking is a hand-off to another process level (See; Thinking Fast & Slow). Scripts represent needed procedures and that to which language anchors.

The usefulness of scripts are their ability to be reused in different ways. Patterns of error may be recognized and communicated more easily by applying topical orientation analysis to show where a representation fails to make sense. Scripts & layers are a foundation for metaphorical analysis.

Various clusters can be loaded into gloss utility templates and assist in analyzing metaphoric dynamics, not covered in this article. Different variations apply to the elements that include, copulas, quantifiers, subjects, predicates & conditionals. Phrasal Templates are also used to test and form quadranyms and can serve as dictionary examples. These phrasal templates express quadranyms through textual samples. There are various phrasal template types and quadranym types. Also beyond the scope of this article.

To conclude, our aim was to bring some understanding of quadranyms and polynyms. The full idea involves unpacking a multi-organizational dynamic system.  The model is a method for commonsense representation that introduces the idea of motivated dynamical-contexts anchoring word-level concepts, we refer to it as Word-Sensibility. Any word in the system can be considered a motivated dynamical-context. Central to this process is the idea that there is the immanent and what necessarily must transcend the immanent, by which this implicates the skills, volition and resources that one has to cope in the world. Word-sensibility is proposed as a hypothetical construct pertaining to affective and conative components. It’s about social instincts, habits, emotions and volition reinforced by the environment.

  • Meaning is in the world. Influencing meaning is in the organism. It’s for the same reason that food is in the world, because organisms eat food.
Habit is the Social Glue

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