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• 1. EROS Word-Sensibility Model 1 A Quick look at the Word-Sensibility System EROS Preview
• 2. Word-Sensibility Introduction 2 EROS • “I will know x as soon as I walk through the door.” 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’ll refer to this knowledge as the Situational Context. Consider the statement below…
• 3. Word-Sensibility Introduction 3 EROS In the Word-Sensibility approach, a prediction is one thing, a response is another. We introduce the idea of the Dynamical Context represented using units of responsiveness. The responsiveness of units to conditions are virtual adaptations to the environment. Consider units of responsiveness represented below. Units of Responsiveness: • 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 • Map the word {through} to Space • Map the words {as_I, as_soon, will} to Time • x = “I will know as soon as I walk through the door.” The textual elements are mapped to responses.
• 4. Word-Sensibility Introduction 4 EROS • 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 • Topic Name: Locomotion(x) 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 Shorthand: (∀x) door(x) ⟹ [Open(passage) ⊇ Close(barrier)(x)] You can think of each responsive unit as being 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. • x = “I will know as soon as I walk through the door.” • Map the words {I, know} to Mental • Map the word {walk} to Locomotion • Map the word {the_door} to Door Responses calibrate to one another thus forming a dynamical context.
• 5. {…} = a dimensional cluster of related terms. •The superset represents the source condition. •The subset represents the target variable. Word-Topics & Quadranyms 5 Sate(hungry) Starve(food) = x Available(consume) Deplete(resource) = x source target eat1 eat2 EROS Word-Sensibility centers around ways to anchor the responsiveness of words in a system. That is, the ability to act quickly and positively to situations in the world. We represent responsiveness in word-topics. Word-Topics (word sense realms) help organize, characterize and summarize lexical information. The model represents word-topics in four dimensions. We refer to these word-topic dimensions as Quadranyms. eat1 source: the subject hungry is predicated on Sate. eat1 target: the subject food is predicated on Starve. eat2 source: the subject consume is predicated on Available. eat2 target: the subject resource is predicated on Deplete. Conditions: affordance is the source and its utility is the target. 1. ∀(x) eat1(x) → [Sate{…)(hungry{…}) ⊇ Starve{…}(food{…})x] 2. ∀(x) eat2(x) → [Available{…)(consume{…}) ⊇ Deplete{…}(resource{…})x] Consider examples eat1 and eat2. Each represent a quadranym word sense of the word eat, and represent a discrete orientation of responsiveness.
• 6. Intersubjectivity & Intimating Mental States 6 EROS Consider eat as a responsive unit. Below, Marie targets the object variable food {chocolate mousse}. Her source condition is hungry. When the source condition is shared with another it is called the coherent bias. It is an intersubjective dynamic. What is required here is the most basic topic and its source condition, a condition that the agent and the patient can share. Any other knowing requirements involves nesting more Word-Topics. • hungry (coherent bias) anchors orientation • food (conditional) is the target variable • Predicates (Sate, Starve) provide function • Subjects (hungry, food) provide arguments In the model, once motivated, the listener’s intention is to find cues in the content so to sync with oscillating coherent and conditional factors. • (∀x) eat(x) ⟹ [Sate(hungry) ⊇ Starve(food)x]
• 7. Independent & Dependent Variables 7 Utility = Sate Weight Crave = Sate 1.0 Nutrition = Sate 0.6 Growth = Sate 0.5 Life = Sate 0.4 starve x 1 2 3 4 5 6 7 8 9 0 90% 70% 50% 30% 10% Utility: Contextual Specification more starve Less starve The Coherent Bias = hungry sate y EROS less crave more crave A word-topic is a specific kind of response. Consider the eat1 response to “I want chocolate mousse!” Responsiveness is represented as follows; the dependent variable responds to the independent variable by changing between several potential utilities to better specify the dependent variable being measured. In eat1, starve is depleting energy. Hungry is the zero-point anchoring sate as the respondent. Food is the target topic. (∀x) eat1(x) ⟹ [Sate(hungry) ⊇ Starve(food)x] Target Topic = food {chocolate mousse …} (20% ,8.2) = low need, high desire In Marie's situation (eating a dessert) starve may may begin at a low rate of 20% and sate’s utility crave may dial in at a high rate 8.2. Hungry is your body telling you it's time to eat. Starve is when you burn more energy than you take in. In this micro-topic, hungry for something anchors the sense of measure between sate and starve. If Marie is diabetic or in someway needing what she craves, then starve will indicate the need for the food (she craves) by an increase in its percentage.
• 8. Motivate-Potential Cause: Starve Orientations & Word Senses 8 EROS Topic Name: Eat(x) Weights (∀x) eat1(x) ⟹ [Sate(hungry) ⊇ Starve(food)x] 1.0 (∀x) eat3(x) ⟹ [Swallow(mouth) ⊇ Chew(food)x] 0.7 (∀x) eat4(x) ⟹ [Intact(chew) ⊇ Fragment(substance)x] 0.6 (∀x) eat2(x) ⟹ [Available(consume) ⊇ Deplete(resource)x] 0.5 (∀x) eat5(x) ⟹ [Stable(corrode) ⊇ Disintegrate(material)x] 0.1 Word-topics are virtual orientations shared between people. Just like there are different word senses, there are different orientations of micro-topics. Consider the different orientations for the topic eat below. Coherent Bias (subject states) = {hungry, mouth, chew, consume, corrode} Consider the assertion; “My car eats up gas.” The orientation eat1 implies the car is hungry. The orientational process must identify any false path that may be educed from the coherent bias. Sensing that the car must be hungry is a mistake that a child can make. Consider how inhibitors, taboo and humor might function as part of this process. Respondent: Sate Judge-Actual
• 9. Units, Scripts, Layers & Polynyms 9 EROS We appreciate your Interest and welcome any feedback. Thank You J Topic Name Expansion Reduction Object Subject 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 5 Dimensions × Relations of Locations = [space, door, distance, direction, container] Quadranym units when linked together form scripts and when stacked form nested hierarchical layers. Word-sensibility analysis is about matching quadranym units to words and sentences in text. Units, scripts and layers reform or reorganize to match the given text. Finally, we introduce Polynyms: dimension layers of any number that form domain systems as in the example below.
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