Text that is written in Natural Language is open to interpretation. There are many formal statements that can be said to interpret a given Natural Language text. Can we determine which formal representation is correct? What about most useful?
The obvious answer to these questions is, "no". There is no such thing as a conclusive interpretation. If there was, then Natural Language wouldn't be ambiguous in the first place!
So we're all doomed to constantly misinterpret each other forever, right? No? We humans use Natural Language all the time, and usually figure out what the other person actually means!? How do we do it? Are we all just really good at guessing?
No, we have something better: context.
Context exists both in and around Natural Language text. Context determines which formal meaning is used to interpret the text. If we don't know which context is appropriate, there may be clues in the text itself that help us construct one that is useful or correct.
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I've been trying to work out an approach to language processing that interprets text into logical formalisms (arbitrary meaning). I call them "Stories". A Story is an arbitrary interpretation of text. A Story is never conclusive: instead it is used as arbitrary context to interpret the next text. I call this process "Backstory".
We could even do the process backwards, and "write" an arbitrary formalism (meaning) in the same language/style/voice as a previously interpreted Story.
Given enough example instances of Story, we should be able to read and write to each other through explicitly shared context. I call this process "Empathizing". I call my idea the Story Empathizer.
I'm definitely out of my depth when it comes to the details, though...
I find humans have variation in ability for this as well though. Like some people need waaay more context, and need everything spelled out in granular detail to understand a topic, vs others who can more easily adapt, pick up clues and other relevant context information.
That's definitely true. I also suspect that holding too much potential context can be counterproductive, because then you have too many options to choose from. This happens a lot with jokes: there are a lot of unique backstories offered by different pop culture references, and pop culture is quickly diversifying to an overwhelming size. There is a lot of entropy in human expression.
The good news is that context can sometimes merge stories together. When we do explicitly find shared context, we tend to leverage that knowledge.
My idea is about offloading as much of this process as possible to a computer. We would still need to choose backstories, but the rest could be done in plain view, leveraging the incredible speed and memory size computers have.
The obvious answer to these questions is, "no". There is no such thing as a conclusive interpretation. If there was, then Natural Language wouldn't be ambiguous in the first place!
So we're all doomed to constantly misinterpret each other forever, right? No? We humans use Natural Language all the time, and usually figure out what the other person actually means!? How do we do it? Are we all just really good at guessing?
No, we have something better: context.
Context exists both in and around Natural Language text. Context determines which formal meaning is used to interpret the text. If we don't know which context is appropriate, there may be clues in the text itself that help us construct one that is useful or correct.
---
I've been trying to work out an approach to language processing that interprets text into logical formalisms (arbitrary meaning). I call them "Stories". A Story is an arbitrary interpretation of text. A Story is never conclusive: instead it is used as arbitrary context to interpret the next text. I call this process "Backstory".
We could even do the process backwards, and "write" an arbitrary formalism (meaning) in the same language/style/voice as a previously interpreted Story.
Given enough example instances of Story, we should be able to read and write to each other through explicitly shared context. I call this process "Empathizing". I call my idea the Story Empathizer.
I'm definitely out of my depth when it comes to the details, though...