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How does the parse tree get represented? Who or what chooses the mapping? They have to be different every sentence. E.g., the word "can" maps onto many differen

by tgv 4y ago

How does the parse tree get represented? Who or what chooses the mapping? They have to be different every sentence. E.g., the word "can" maps onto many different meanings and functions. Your question is about internal representation, but skips the process, and is about a representation that isn't the end of the process either. The idea, common to many of competing theories of human NLP, is that language is to a large extent modular (e.g. recognizing words can be done independent of e.g. a sentence context), while on the other hand, the context (often thought of as feedback from other modules/brain parts) has some influence over it (e.g. the recognition of a misspelled word is easily influenced by the sentence it appears in). The "layers" of language processing then are word, structure, meaning, and pragmatic. The brain being incredibly interconnected, this picture is simplistic, but both behavioral experiments and neurological findings support it to a large extent: it's not one big neural soup. There are other, related processes, which are often left out when talking about language, but e.g. gesture and visual information definitely play a role as well. The parse tree does exist somewhere in the brain, but it cannot map to connections. Instead, it will have to encode some relation between working memory, the large (and largely ununderstood) space of our mind that contains the concepts, and the role in the sentence and further context. Encoding the "pointer address" (because that's what your model sounds like) of the word is simply not enough. It will be passed along at some point, but is in itself insufficient. E.g. in I want the red ball, not the blue ball, the receiver needs to distinguish objects referenced by the exact same word with the exact same meaning. Having a pointer to the lexical concept ball will not help. I think showing "that its wrong" only gives insights into your own mental model, not into language processing in general. It is too undeveloped for further discussion.

jjtheblunt 4y ago
>The parse tree does exist somewhere in the brain Is that even clear? Parse trees are a discretization of stochastic grammars, for example, with probabilities of "production rules" set to either 1 (so you get the right hand side rewrite) or 0 (so you don't get that as an option). Disclaimer: worked on stochastic grammars for computation in recognizers 20 years ago, not sure how the defs might have drifted since then.
- solarmist 4y ago
  Nope. The representation in the brain seems to be like a parse tree, but it is a statistical AST, not how we think of it with computers. If you have a basis in stochastic grammar, you should look into Operator Grammar. It is a very compelling description of grammar based on set theory and maps very neatly to the best neuroscience I'm aware of.
  - jjtheblunt 4y ago
    oh cool...thanks for the recommendation, as i'm stale in that space for sure
solarmist 4y ago
This is an extremely dismissive comment, plus you are way off basis that it is underdeveloped. It is just new/underdeveloped for OP. Literal books have been written about this exact idea by Zellig Harris. The reason it is not more well known is that Chomsky (his student) became famous and reinterpreted a couple of his ideas (using mathematical logic for the basis of his work rather than Harris's set-theoretic base). But while Chomsky had to keep creating castles in the sky (including the idea of universal grammar/external metalanguage) to patch the holes in his theory Harris's theory needs none of that and is self-organizing and self-sufficient. An alternate (and consistent with OPs idea) list of layers of processing a language. 1. Phonemic distinctions. 2. Words and morphemes, with their main meanings. (The word with its morphemes is an umbrella that also encompasses gesture, visual, and any other perceptual information related to the word. The words we use are not separate entities from our experiences. In the same way, mirror neurons aren't a thing. We mirror because we only have one set of machinery, and so to understand the other person is literally to hallucinate experiencing it to some degree.) 3. Word dependencies (the argument requirement of each word). 4. The selection of each word (the dependencies that have greater than average likelihood). 5. The canonical or preferred linearization (word order) and its alternatives. 6. The main reductions (variant shapes of words), their domain (a particular word or all words in a position) and the conditions for applying them. Linearization is one of the last steps of producing a sentence. According to Operator grammar theory, most language complications come from the simplifications we produce as shortcuts (such as pronouns, contractions, conjunctions, etc). So your first two questions are just the memory of the words/ideas/objects/etc combined with an operator and our perceptions in context do the choosing. Very much like an AST, but statistical (for word likeliness), as opposed to deterministic. The most differences start to arise when we start the process of moving that sentence externally. We apply reductions and linearization which are language specific. The intersection between HPCT (Hierarchical Perceptual Control Theory) and Operator Grammar explains the details pretty well. And Zellig Harris presents a complete analysis of English Grammar in "A Grammar of English on Mathematical Principles". It is a very different way of looking at language grammar, which makes much more sense to me biologically he spends significant time showing it can also reproduce traditional grammar. I.e. Traditional grammar could be viewed as a change of basis from Operator grammar. Take a look at An embodied grammar of words[1] for an overview. [1] https://www.researchgate.net/publication/324362482_An_embodied_grammar_of_words https://www.researchgate.net/publication/324362482_An_embodi...
IIAOPSW 4y ago
>How does the parse tree get represented? >Your question is about internal representation, but skips the process, and is about a representation that isn't the end of the process either. I think you missed the essence of my conjecture. I'm not "skipping the process". I'm speculating that there is surprisingly little process to be done at all because language is "close to the metal" (to borrow an analogy). Here's a simple but limited language. We can have electronic components like batteries and resistors and inductors as our basic noun objects, and our relational verb words are all just telling us if subjects and objects are connected in parallel or series. The only adjectives are numerical pre-fixes for amounts of voltage/resistance/whatever. A "." will suffice as a grammatical particle to mark "end of object". Its a bit verbose and cumbersome, but I can describe whatever circuit I want in these terms. In fact, the parse tree of this grammar can literally encode any loop-free circuit, with sibling branches in the parse tree corresponding to parallel connections and nested branches in the tree corresponding to series connections. I could literally use this grammar and vocab as a file format for an FPGA if I wanted to. One doesn't even need a fully general computation or additional reasoning abilities to "understand" this circuit language. A machine that physically builds the connections according to the grammatical rules as the words stream in would suffice. The semantics of this toy language don't require any further processing or translation. Now here's the toy brain I envision. Purpose follows a tree structure. Things happen and things exist because someone wills them to be. When you get paid to work, the work serves the will of the company. When you do it for yourself, its your free will. When your computer does something, its because of your will (never its own free will). Each person is both a source of will power and a thing that exists as a matter of ground truth. Thus we can think of a person as a battery in a simplified analog model of our world. For example you can describe a short circuit of one of these person-batteries with a mutually recursive circuit-statement: "I am because I want to be. I want to be because I am." This is a vacuous truth. You can also make more sophisticated paths from will power (want) to common ground (facts) as a way to encode a more complex scenario. All you have to do is put intermediate things in the path between "I want" and "I am". "I want [A] because [B] so that [C] or [D] which leads to [E] ... so that I continue to be." The full semantics of the objects in the broader context may or may not be known, but it doesn't matter. Once you have a spoken-circuit encoding the immediate dynamics of motivations (batteries in the circuit) and how they relate back to ground (resistors and inductors encoding a simplified simulation of a scenario), you can start modeling these dynamics as a literal analog electrical circuit. In the special case of one power source in the circuit with enough voltage to overwhelm any other, this system of analog simulation should reduce to the ordinary semantics of True/False reasoning. The big voltage represents tautology. If A implies B, and A is connected to the tautology voltage, B is now at the tautology voltage. All sorts of semantics and reasoning calculi can be encoded in this way. Its a powerful paradigm. Does it actually happen in the brain though? I have no idea. I realize in retrospect it sounded like I was suggesting absolute dictionary word to single neuron translation. That's not what I meant. What I'm really suggesting is more like a wetware FPGA that does abstract reasoning about people/causation with analog circuit models. It so happens those circuits can be encoded into a spoken transfer format in a very literal way. Now that I've elaborated further, is that at least developed enough to discuss?

by tgv 4y ago

jjtheblunt 4y ago

>The parse tree does exist somewhere in the brain Is that even clear? Parse trees are a discretization of stochastic grammars, for example, with probabilities of "production rules" set to either 1 (so you get the right hand side rewrite) or 0 (so you don't get that as an option). Disclaimer: worked on stochastic grammars for computation in recognizers 20 years ago, not sure how the defs might have drifted since then.

solarmist 4y ago
Nope. The representation in the brain seems to be like a parse tree, but it is a statistical AST, not how we think of it with computers. If you have a basis in stochastic grammar, you should look into Operator Grammar. It is a very compelling description of grammar based on set theory and maps very neatly to the best neuroscience I'm aware of.
- jjtheblunt 4y ago
  oh cool...thanks for the recommendation, as i'm stale in that space for sure

solarmist 4y ago

This is an extremely dismissive comment, plus you are way off basis that it is underdeveloped. It is just new/underdeveloped for OP. Literal books have been written about this exact idea by Zellig Harris. The reason it is not more well known is that Chomsky (his student) became famous and reinterpreted a couple of his ideas (using mathematical logic for the basis of his work rather than Harris's set-theoretic base). But while Chomsky had to keep creating castles in the sky (including the idea of universal grammar/external metalanguage) to patch the holes in his theory Harris's theory needs none of that and is self-organizing and self-sufficient. An alternate (and consistent with OPs idea) list of layers of processing a language. 1. Phonemic distinctions. 2. Words and morphemes, with their main meanings. (The word with its morphemes is an umbrella that also encompasses gesture, visual, and any other perceptual information related to the word. The words we use are not separate entities from our experiences. In the same way, mirror neurons aren't a thing. We mirror because we only have one set of machinery, and so to understand the other person is literally to hallucinate experiencing it to some degree.) 3. Word dependencies (the argument requirement of each word). 4. The selection of each word (the dependencies that have greater than average likelihood). 5. The canonical or preferred linearization (word order) and its alternatives. 6. The main reductions (variant shapes of words), their domain (a particular word or all words in a position) and the conditions for applying them. Linearization is one of the last steps of producing a sentence. According to Operator grammar theory, most language complications come from the simplifications we produce as shortcuts (such as pronouns, contractions, conjunctions, etc). So your first two questions are just the memory of the words/ideas/objects/etc combined with an operator and our perceptions in context do the choosing. Very much like an AST, but statistical (for word likeliness), as opposed to deterministic. The most differences start to arise when we start the process of moving that sentence externally. We apply reductions and linearization which are language specific. The intersection between HPCT (Hierarchical Perceptual Control Theory) and Operator Grammar explains the details pretty well. And Zellig Harris presents a complete analysis of English Grammar in "A Grammar of English on Mathematical Principles". It is a very different way of looking at language grammar, which makes much more sense to me biologically he spends significant time showing it can also reproduce traditional grammar. I.e. Traditional grammar could be viewed as a change of basis from Operator grammar. Take a look at An embodied grammar of words[1] for an overview. [1] https://www.researchgate.net/publication/324362482_An_embodied_grammar_of_words https://www.researchgate.net/publication/324362482_An_embodi...

IIAOPSW 4y ago

>How does the parse tree get represented? >Your question is about internal representation, but skips the process, and is about a representation that isn't the end of the process either. I think you missed the essence of my conjecture. I'm not "skipping the process". I'm speculating that there is surprisingly little process to be done at all because language is "close to the metal" (to borrow an analogy). Here's a simple but limited language. We can have electronic components like batteries and resistors and inductors as our basic noun objects, and our relational verb words are all just telling us if subjects and objects are connected in parallel or series. The only adjectives are numerical pre-fixes for amounts of voltage/resistance/whatever. A "." will suffice as a grammatical particle to mark "end of object". Its a bit verbose and cumbersome, but I can describe whatever circuit I want in these terms. In fact, the parse tree of this grammar can literally encode any loop-free circuit, with sibling branches in the parse tree corresponding to parallel connections and nested branches in the tree corresponding to series connections. I could literally use this grammar and vocab as a file format for an FPGA if I wanted to. One doesn't even need a fully general computation or additional reasoning abilities to "understand" this circuit language. A machine that physically builds the connections according to the grammatical rules as the words stream in would suffice. The semantics of this toy language don't require any further processing or translation. Now here's the toy brain I envision. Purpose follows a tree structure. Things happen and things exist because someone wills them to be. When you get paid to work, the work serves the will of the company. When you do it for yourself, its your free will. When your computer does something, its because of your will (never its own free will). Each person is both a source of will power and a thing that exists as a matter of ground truth. Thus we can think of a person as a battery in a simplified analog model of our world. For example you can describe a short circuit of one of these person-batteries with a mutually recursive circuit-statement: "I am because I want to be. I want to be because I am." This is a vacuous truth. You can also make more sophisticated paths from will power (want) to common ground (facts) as a way to encode a more complex scenario. All you have to do is put intermediate things in the path between "I want" and "I am". "I want [A] because [B] so that [C] or [D] which leads to [E] ... so that I continue to be." The full semantics of the objects in the broader context may or may not be known, but it doesn't matter. Once you have a spoken-circuit encoding the immediate dynamics of motivations (batteries in the circuit) and how they relate back to ground (resistors and inductors encoding a simplified simulation of a scenario), you can start modeling these dynamics as a literal analog electrical circuit. In the special case of one power source in the circuit with enough voltage to overwhelm any other, this system of analog simulation should reduce to the ordinary semantics of True/False reasoning. The big voltage represents tautology. If A implies B, and A is connected to the tautology voltage, B is now at the tautology voltage. All sorts of semantics and reasoning calculi can be encoded in this way. Its a powerful paradigm. Does it actually happen in the brain though? I have no idea. I realize in retrospect it sounded like I was suggesting absolute dictionary word to single neuron translation. That's not what I meant. What I'm really suggesting is more like a wetware FPGA that does abstract reasoning about people/causation with analog circuit models. It so happens those circuits can be encoded into a spoken transfer format in a very literal way. Now that I've elaborated further, is that at least developed enough to discuss?