Whatever happened to those flower arrangements?

They meant a lot more than I let on. (See here and here)

GitHub - where are my stars?

I have a hard time finding the Narwhal repo on GitHub if I search for it using tags. Other repos with thousands of stars are better placed in the search results. Poor Narwhal has something like only 8 stars total. So I just looked at an "nlp"  project that had 1.4K stars and it was a list of links to documents and source code on nlp.
Really? That is 2 orders of magnitude more stars than Narwhal and zero independent content. I think this shows that stars are only one metric and GitHub [Microsoft are you listening] should provide other ranking methods.

Are adjectives just simplified parts?

In the format where a context can have a 'modifier' and can also have a 'part', the ambiguity is strongest when the 'part' has just one modifier; or when the original whole has only one part and its modifiers are more simply handled as as modifiers of the original whole.
For example you can talk about the max speed of a car as a simple modifier of 'car' or you can make it one of the modifiers of the 'performance characteristics' - part of the car. If you were going to talk about acceleration and other performance characteristics it would be better to have the max speed not be a simple modifier of 'car'.

Language Principle: you can say whatever you like

I like the idea that you can intend  to speak about something a certain way, even when it is not natural to the thing. For example "the collection of circle" or "you know, I wish last week". Not only can you intend to speak this way, you can sometimes even make a bit of sense out of the nonsensical. This gives rise to a general principle, which is that you can speak about something any way you choose.

This principle might help the denouement of the adjective/part distinction.  Let me explain: it is not completely crisp that having "parts" (namely sub contexts) is not the same as having modifiers (individual attributes). Take for example a "car" that has a color attribute. But how fast the car can go, is not really a simple modifier. It is a modifier of the car's "performance" sub-context [I call sub-contexts "parts"]. I don't think it helps the study of philosophical ontology, to confuse the self attributes with the part's self attributes.

Nonetheless, the principle says: you can talk about a part's attributes as if they were the self attribute of the original object. You can talk about maximum speed as an attribute of the car. In that sense: does it matter if you get the ontology wrong? Perhaps it does not matter if you are trying to communicate, because subtle context switching is going on all the time when we interpret speech.

But you have to be more careful if you are writing a program. The "subtle" context switching can come later. In the shorter term, you must distinguish carefully between modifiers and parts of a context "object".

Teasing apart the structure of a "context"

Starting with a vague "lump" of desire for conversational context to be defined, I have come so far as to define a ContextFrame (a template) and a ContextRecord (A snapshot of a partially filled template). Now I am getting clearer, slowly day by day, on some of the details. So we have

ContextFrame:
   ID (with a self vocab)
   ENV (another ID)
   MODS (each with a tree of vocabs)
   PARTS(each with its own ID)
   RELS (narrative structures used to fill MODs)

ContextRecord:
   (a printout of ID and MOD values)

Generally I will assume a tree of ContextFrames, doubly linked via ENV and PARTS and with a single parent. Later we can talk about dynamic re-assignment of ENV.
**********************************************

Where it is starting to get subtle is in the observation that a linked collection of ContextFrames can include both a tree of ID vocabularies and a collection of trees: one per MOD, per each frame.

To be honest, although I am pretending that there is nothing real or anatomical about the format that I am defining, in the back of my mind I hope there is something correct about these definitions that transcends my current programming needs. I am being merciless with myself, trying to be as clear as possible. So two types of trees emerge within definitions and I wonder about neural anatomy. Since the contexts are almost nothing but 'wiring' it is easy to imagine it.

Three weeks into retirement and coding resumes

I have been trying hard to force myself back into real thoughts about real subjects - related to my 'language automation' interests. Finally got close enough that I am about to start writing new Python code - a milestone I have been hoping to achieve, three weeks after quitting work and going into a full-time "vegetative" state.
Update: A week later I have a working context object containing lots of trees with VARs. Now I have to start passing text through it to explore and implement switching between sub contexts.
Update 2: [bearing in mind that I am trying to justify having retired and trying to reassure myself that it won't be that bad] Another week and I have a reasonable starting implementation of ContextFrames and am about to start implementing ContextRecords, which save instances of attached Frames.

Did I mention the insight that a frame attachement is simply the evaluation of certain variables of a frame? So eg the base point of a Frenet Frame is "attached" when it is set to the position on a curve at time t.

More Geometry of Language: Attaching ContextFrames at a point of a Conversation

My overall metaphor has been that a narrative structure is fit to a piece of incoming text. I am changing that a bit and making the ContextFrame the entity 'fit' to incoming text. The metaphor is that understanding text is a frame attachment.

The 'relations' in a ContextFrame are narrative structures used to create a record (a ContextRecord) from the template of the frame and the incoming data. This filling of the record is the exact equivalent to what happens in differential geometry when attaching or 'adapting' of a frame to data. Eg, when a Frenet frame is attached to a curve, that  means setting a base point and direction vectors equal to values.

As I pointed out in Best Models, the Frenet frames are iterative and progressive. They stop after 3 dimensions because it is a complete coordinate system. However contacts of line, circle, spiral, and all higher order curves are all available for fitting with additional iterations, using higher and higher order derivatives. In the same way the ContextFrames can be more or less attached - although I am not clear about iteration order and the need to start with the most general frame.

The point is that ContextFrames are attached to language data in the same way as geometric frames are attached to geometric data - by evaluation. The template represented by the frame's definition is filled in to create an attachment. I believe the metaphor is good.

Update: Barb asks good questions: "How is this unique? What is if good for?"
Answer is: it is a new way to talk about reality with context replacing object as the center of discussion. Also, it closes the loop with the mathematical ideas of adapted frames, where now language takes its place besides visual reality as subjects of geometric thinking. What it is good for is to organize context within the language and data part of a chatbot program, rather than organizing context within the program logic of a chatbot.