Coming Soon
Quadranym Word Vectors
Consider the Polynym, ‘Relations of Locations’. Now consider how a Polynym layer might cycle its unit. Consider examples: She lives far from here. I’m going across town for lunch. I left my heart in San Francisco.
Q-units act as a kind of deictic center for a contextual trace in relation to which a word sense is to be interpreted.
Layer: Condition (topic name) Distance:
Script cycles 3:
- First Unit:[Far(position) ⊇ Near(relation)]
- Second Unit:[Far(relation) ⊇ Near(place)]
- Third Unit:[Far(place) ⊇ Near(position)]
CAPTURE:
- IF Far(self) THEN Near(relation)
- IF Far(heart) THEN Near(relation, place, San Francisco)
- IF Far(zero point, San Francisco ) THEN Near(position, heart)
Polynyms strategically organize the responsive dynamics of topics.
BELOW IS VARIOUS NOTES AND UNEDITED MATERIAL
pc1. Pseudo Code Example
Word-Sensibility & Ecological Reasoning
Making & Tweaking Scripts
Analyzing relations of locations in sentences. This idea is for database operators who review scripts and tweak them as necessary. Prompts allow for improvements. It then generates analysis reports on scripts.
* “My friend lives far from here.”
IF far THEN FUNCTION f(x) = Distance(x) RETURN ARGUMENT INPUT Distance(x)
PRINT:
[qFunctions (modes) {Far, Near} qArguments (states) {position, relation}]
PROMPT: RETURN ADDITIONAL CONTEXT ELEMENTS:
PRINT:
Object(x) Place(x)
(note, the above is from a situational context polynym. it deals with subjects, objects, goals and deictic elements)
PROMPT: RETURN INPUTS AS ARGUMENTS:
PRINT:
[Far(position) _ Near(relation)]::[Far(relation) _ Near(object)]::[Far(object) _ Near(place)]::[Far(place) _ Near(object)]<stop>
INTERPRETATION OF PROPOSITION:
PRINT:
IF: Far_relation = Far_object THEN Far_place = Near_object
(note, above are all elements of the ontological realm of distance. these elements are pulled from a cluster and are virtually attended in the script (position is primary but unattended). as a composition, it would be interpreted something like, “the object is at a place far away from here.” (interpretations like this could involve phrasal templates.))
PROMPT: RETURN ADDITIONAL CONTEXT ELEMENTS:
PRINT:
Person(x)
(note, this can be chosen from situational context elements)
RUN PARSE SCRIPT W/CONTENT INPUTS:
(note, this is pulled automatically from sentence and unit default)
PARSE UNIT Person(x): (note, unit is a single quadranym)
FROM: [Far(position) TO Near(x)] = relation_qFunction = person
RETURN FUNCTION f(x):
PRINT:
Friend(x)
PARSE FLUX Person(x): (note, flux is between quadranyms)
FROM: [Near(FRIEND)] TO [Far(x)] = position_qFuntion = person
RETURN FUNCTION f(x):
PRINT:
Self(x)
CONDITIONAL FUNCTIONS W/ARGUMENTS:
PRINT:
Self(position) → Friend(relation)
RETURN:
IF RELATION INFERS ACTION RUN IN RELATION POSITION ELSE RUN RELATION IN OBJECT POSITION (i.e., noun)
PRINT SCRIPT
[Far(SELF) _ Near(relation)] | [Far(relation) _ Near(FRIEND)] | [Far(FRIEND) _ Near(place)] | [Far(place) _ Near(FRIEND)]<stop>
PROMPT: IF POSITIVE ASSESSMENT SAVE SCRIPT
PROMPT: RUN ANALYSIS REPORT THEN PRINT:
PROPOSITION CONDITIONAL:
IF: Far_relation = Far_friend THEN Far_place = Near_friendOUTPUT QUADRANYM:
q4:[E_Far(S_place) _ R_Near(O_friend)]
OUTPUT FRAME RANGE = (1 of 4, unit 4)INPUT QUADRANYM:
q1[E_Far(S_position_self) ⊇ R_Near(O_relation_x)]
INPUT FRAME RANGE = (1 of 4, unit 1)POLYNYM: TRIONYM REFORMULATION
OF
SITUATIONAL CONTEXT FUNCTIONS = {person, place, object}:
1. Person(x)
2. Time(x)
3. Discourse(x)
4. Place(x)
5. Purpose(x)
6. Subject(x)
7. Object(x)
8. Inference(x)
9. Goal(x)Q ATTRIBUTES:
SUPERSET/SUBSET CLUSTERS:
E(s)_Superset {position, self, far, near, friend, object, place, relations…} R(o)_Subset {relation, object, place, friend}
THE SUPERSET HEMISPHERE = E(s):
Far = E, active_potential_spatial_mode Position = S, active_actual_temporal_state
ABOVE IS ABOUT THE QUESTION
(note, active refers to more experience necessary to obtain a sense of a situation. in other words a unit is started but not complete. actual refers to the temporal center of the process. it reflects the metaphysical notion of being presently active with input power from the environment.)
THE SUBSET HEMISPHERE = R(s):
* Near = R, passive_actual_spatial_mode
* Relation = O, passive_potential_temporal_state
THE ABOVE IS ABOUT THE ANSWER
(note, passive refers to no more experience necessary. a sense is complete and now can be used in a variety of ways. it reflects the notion of a potential power to be utilized in relation to a situation)
Q CYCLE FUNCTIONS:
* E(s) RUN CYCLE FUNCTION: x is questioned = Far(x)
* R(o) END CYCLE FUNCTION: x is answered = Near(x)
(note: an active_actual _temporal state is the metaphysical notion of Q agency. it represents active_actual_power only in the present of time. passive_potential_temporal_state refers to a mental object of the agency becoming in time. it represents passive_potential_power. a rock can become a potential tool, but it only has power when acted on. once the tool is acted on, it is then active_actual and part of the agency itself)FIRST ORDER CONCLUSIONS: qArgument: ∀x(Self(x) → Relation(x, friend) qFunction: ∃x(Friend(x) → Far(x, place)
PHRASAL TEMPLATE: NO RETURN HIERARCHICAL STRUCTURE: NO RETURN NESTED INFERENCES: NO RETURN
(note, a Q can be unpacked much further with deeper attribute analyses.)
WIP
Goals for Q Analysis
How does the Q fit into the Natural Language Processing (NLP) picture?
NLP has two basic components:
- Natural Language Understanding (NLU)
- Natural Language Generation (NLG)
NLU is about mapping input to useful representations and about analyzing different aspects of the language.
- Here, the Q supplements semantic processes to analyze open text.
NLG is about text planning, sentence planning and text realization.
- Here, The Q formulates scripts and interacts with human responses.
Notes:
- Far(relation) + Far(object) + Far(place) = Near(object)
- Far(relation) + Far(friend) + Far(place) = Near(friend)
There are many ways to utilize scripts. Q scripts may utilize relationships between ranks and columns to better fit a dynamical context. Polynyms fill out the ranks and quadranyms fill out the columns. That is, polynyms are layers and quadranyms are units. Units can form scripts on different layers. Scripts can run simultaneously with other scripts. Slower scripts constrain faster scripts. Each column tends to provide a kind of pervading sense from which dynamic relationships can be made. Also, source and target opportunities form in columns between ranks effectually adding metaphoric dynamics.
(Note: Realm vs Domain. A domain is a hypothetical construct in the Q. Still, The Domain is considered The Thing to belong to. For instance, the polynym above is not The Spatial Domain but a spatial realm of The hypothetical Spatial Domain.)
A wonderful way to contrast Q thinking from more deliberative thinking is to consider the cosmological view of space. Physicists claim that space is not empty, that space itself is something like a fabric or something more.
In the model page, we discussed how the Q-unit illustrates basic temporal and spatial dynamics. It’s easy to consider how the Q model’s spatial sense is centered on the mode terms, expansive & reductive. It’s a little harder to see how the state terms subjective & objective center on the temporal sense.
Briefly, the subjective sense is about the zero-point (constant) of nowness where the objective sense (or target) is about the ever changing dynamic of eventfulness. This idea nicely correlates with our Q-topic space; emptiness is the zero-point (constant) while between is the ever changing dynamic sense.
Discrete Functions:
- Potentials = Sate(substance)
- Actuals = Starve(food)
Above, an iteration of the subjects topic; eat(x) → hungry ⊇ food(x).
New example: container
- (∀x) container(x) ⟹ [Out{…}(empty{…}) ⊇ In{…}(full{…})(x)]
Q-topic: A Q-topic is an experience rendered a contextual artifact i.e., dynamically actual and situationally potential. In other words, there are many ways to approach a topic. Situations may change but the dynamic sense remains the same. Consider the topic container, it may be a cup of coffee or a social belonging, as in, Jan is in the club. Q analysis makes any word a topic in-so-far as it applies to its basic dynamics. A word topic can change intensional roles of its dimensions but retain relational dynamics.
(note: The term topic is the generic term referring to the extension of an episodic experience. It has many perspectives that can change depending on conditions. Conversely, an intensional framework can stay the same and the extensional name can change, just in case it may fit synonymically better with a discourse. When applied to computational lexicography a topic is called the head term.)
(Quadranym: Intrapersonal & Specific Granularity)
Every polynym dimension has an array of senses, each a quadranym. A query produces what will anchor on the subset subject, for instance, in the examples above, food, substance, resource, material are each that – i.e., a potential-becoming that may become the actual-being of the next frame.
For example: eat → food produces the next frame food → x
- x = [Potential_Sate(actual_food) ⊇ Actual_Starve(Potential_substance]
Each unit a discrete point of an iterated function.
Q-units are populated by, quadranyms: four superordinate dimensions of a topic that work to subsume content i.e., cluster of subordinates.
Subordinate cluster: {…}
- (∀x) eat(x) ⟹ [Sate{…}(hungry{…}) ⊇ Starve{…}(food{…})(x)]
Build Intuit 