4 ms·

But the meaning is not in the text (text-as-artifact, the ink on the page), it is strictly in the mind of the reader, accessed by reference to the speaker's exp

by rvense 5y ago

But the meaning is not in the text (text-as-artifact, the ink on the page), it is strictly in the mind of the reader, accessed by reference to the speaker's experience with the language in question. If you don't have something to ground at least part of the symbols in, there is nothing to work from. Any word can be assigned any meaning, and there would be infinite possibilities. No amount of statistics will allow you to work out whether a given word or part of a word refers to a rock, a rabbit or Noam Chomsky's granddaughter. How could it? To be able to do this is called knowing a language, and if all the people who had that skill in the soft tissue of their brain have kicked the bucket, tough luck, you can't get that knowledge back. All (bar none) decipherments of unknown writing systems have been done by reference to related languages, especially the existence of bilingual texts. Computers and statistics are definitely able to help us do that, but if all you have is a page of text with no context, then the information you're looking for is gone.

thom 5y ago
I agree to a very high degree! But I still think that you have greater than zero chance (but not much greater) to make some correct guesses about some very, very basic grammatical forms based on frequencies within other languages. You'd never be able to confirm this, and it's not a very important point. However, I do strongly disagree with the other point in the comment I was responding to, that you could not tell a real language with meaningful words from one with random words. That (entirely made up and not very helpful) problem does seem like it would be amenable to basis statistical analysis.
petters 5y ago
I maintain that it is possible. A rock and a rabbit would occur in different contexts. GPT-3 solves this problem in a limited but impressive way, as I mentioned in a comment below just now.
mcswell 5y ago
Actually, you can get a readout on lexical semantics (the meaning of words) if you have a large enough corpus (much larger than we have for any extinct language). The method uses a vector space of word cooccurrence (look up word2vec). It will tell you what words are semantically similar. What it won't tell you is what those words mean in the real world (unless possibly you have pictures with captions). It's like being able to see mountains in one direction and a lake in another and a valley in another direction; you can say you're going towards one or the other of these features, but (ignoring the sun and stars) you don't know whether that's north, south, east or west.
- rvense 5y ago
  This is... basically what I'm saying? How will you make a translation without knowing what the words mean in the real world? You can get a vector that says 'oiufd' is to 'flarewq' as 'rye8poq' is to 'nmjrewq'. I don't really think that counts as anything like deciphering the mysteries of Harrappa.

by rvense 5y ago

thom 5y ago

I agree to a very high degree! But I still think that you have greater than zero chance (but not much greater) to make some correct guesses about some very, very basic grammatical forms based on frequencies within other languages. You'd never be able to confirm this, and it's not a very important point. However, I do strongly disagree with the other point in the comment I was responding to, that you could not tell a real language with meaningful words from one with random words. That (entirely made up and not very helpful) problem does seem like it would be amenable to basis statistical analysis.

petters 5y ago

I maintain that it is possible. A rock and a rabbit would occur in different contexts. GPT-3 solves this problem in a limited but impressive way, as I mentioned in a comment below just now.

mcswell 5y ago

Actually, you can get a readout on lexical semantics (the meaning of words) if you have a large enough corpus (much larger than we have for any extinct language). The method uses a vector space of word cooccurrence (look up word2vec). It will tell you what words are semantically similar. What it won't tell you is what those words mean in the real world (unless possibly you have pictures with captions). It's like being able to see mountains in one direction and a lake in another and a valley in another direction; you can say you're going towards one or the other of these features, but (ignoring the sun and stars) you don't know whether that's north, south, east or west.

rvense 5y ago
This is... basically what I'm saying? How will you make a translation without knowing what the words mean in the real world? You can get a vector that says 'oiufd' is to 'flarewq' as 'rye8poq' is to 'nmjrewq'. I don't really think that counts as anything like deciphering the mysteries of Harrappa.