[HN Gopher] Depends upon what the meaning of the word "is" is
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Depends upon what the meaning of the word "is" is
Author : feross
Score : 31 points
Date : 2020-08-07 05:43 UTC (17 hours ago)
(HTM) web link (meaningness.com)
(TXT) w3m dump (meaningness.com)
| thelazydogsback wrote:
| I just saw this sign yesterday and was thinking how many ways it
| could be parsed:
|
| "Big golf factory sale opening"
|
| Seems like at least 8:
|
| "Big (golf factory)" vs "(Big golf) factory"
|
| "(factory sale) opening" vs. "factory (sale opening)"
|
| "... opening:V(PresentProgressive)" vs "... opening:N"
|
| Only two of which are semantically likely, and one of which is
| pragmatically likely - unless you're really rich and in the
| market for golf factories.
| clairity wrote:
| bill clinton, is that you?!
|
| but seriously, in contrast to natural language, the article lays
| out the basics of mathematically-based logic, not for its own
| accord, but as groundwork for the more interesting later sections
| talking about context-dependence and reasonableness.
|
| the reason natural language has so much ambiguity is not because
| our brains couldn't have come up with a rigorously logical
| language, but because the world is ambiguous and language
| reflects that.
| foldr wrote:
| >but because the world is ambiguous and language reflects that.
|
| This doesn't seem like a very satisfying explanation. Take one
| particular example of a structurally ambiguous sentence of
| English:
|
| "The company couldn't make the car fast enough".
|
| The two meanings are completely distinct (speed of production
| vs. speed of the car). There's no fuzziness about this
| distinction out there in the world. The speed at which a car
| travels and the speed at which it's made are two completely
| distinct properties.
| eindiran wrote:
| That is an interesting example because it looks like semantic
| ambiguity rather than syntactic ambiguity. But actually it is
| about structure as you commented -- something like this:
|
| [S [DP [D The [N company]]] [VP [AuxP [Aux couldn't] [V
| make]] [DP [D the] [NP [N car] [AdjP [Adv fast] [A enough]]]]
|
| vs
|
| [S [DP [D The [N company]]] [V' [VP [AuxP [Aux couldn't] [V
| make]] [DP [D the] [N car]]] [AdjP [Adv fast] [A enough]]]
|
| Regarding the meat of your comment, it is quite difficult to
| banish all ambiguity from natural language for a variety of
| reasons, but we don't really need to: humans are incredibly
| good at handling linguistic ambiguity. There has been a lot
| of fascinating research on the topic: in particular, I
| recommend reading up about anaphora resolution[0] and garden
| path sentence repair[1], because the literature includes some
| info on what is happening in the brain, which is
| significantly more detailed than what exists for most other
| types of linguistic ambiguity.
|
| All of this ambiguity in natural language is something that
| continues to be huge hurdle for NLP: it turns out that
| fetching the right information from the context to resolve
| all the ambiguities that arise in a single conversation is
| completely non-trivial, despite how easy humans make it look!
|
| An interesting case study in the opposite direction (ie
| attempting to remove ambiguities from natural language) is
| Ithkuil[2]: it is a conlang that attempted to completely
| banish (semantic and lexical) ambiguity and it ended up being
| ridiculously hard to use or learn at all.
|
| [0] https://en.wikipedia.org/wiki/Anaphora_(linguistics)
|
| [1] https://en.wikipedia.org/wiki/Garden-path_sentence
|
| [2] https://en.wikipedia.org/wiki/Ithkuil
|
| If anyone is curious, you can plug those trees into here
| (http://mshang.ca/syntree/) and it will draw them for you.
| But my tree drawing skills are very rusty, so they are pretty
| basic/bad.
| joosters wrote:
| And yet, in the real world, people could use that sentence as
| part of a discussion without causing any confusion or
| ambiguity. Because sentences don't exist on their own, they
| have a wider context which can focus their meaning.
|
| Human language is succinct. We don't generally say twenty
| words when ten would do. If the context made your example
| sentence clear, why would a speaker need to add any words to
| clarify it further?
| RedEdward71 wrote:
| I immediately thought of slick willy as well.
| adrianmonk wrote:
| Also time constraints. It's possible to be much more precise
| even with informal language, but it would take forever.
|
| So, for efficiency, words don't really deliver an idea to the
| listener. Instead, it's assumed that the listener is working
| toward the idea through their own reasoning, and words fill in
| only the necessary gaps to help them get there or to help them
| get there more quickly.
|
| You more or less reverse engineer what their thought process
| must be, then you do a gap analysis between what they're
| probably thinking and what you want them to be thinking, and
| you give them the pieces of info necessary for them to make the
| leap.
| avindroth wrote:
| One of the most influential philosophical concepts for me from
| the last decade was from this very blog called "Nebulosity". It
| just speaks to the nature of reality that is misinterpreted with
| overlays of meaning.
|
| -
|
| 'Nebulosity' refers to the insubstantial, amorphous, non-
| separable, transient, ambiguous nature of meaningness.
|
| From a distance, clouds can look solid; close-up they are mere
| fog, which can even be so thin it becomes invisible when you
| enter it.
|
| Clouds often have vague boundaries and no particular shape.
|
| It can be impossible to say where one cloud ends and another
| begins; whether two bits of cloud are connected or not; or to
| count the number of clouds in a section of the sky.
|
| If you watch a cloud for a few minutes, it may change shape and
| size, or evaporate into nothing. But it is impossible to find an
| exact moment at which it ceases to exist.
|
| It can be impossible to say even whether there is a cloud in a
| particular place, or not.
|
| [from] https://meaningness.com/nebulosity
| Rumperuu wrote:
| Related: https://en.wikipedia.org/wiki/E-Prime
| nojs wrote:
| It's interesting to think how fundamentally impossible it is to
| parse a sentence without a background corpus of knowledge about
| the world:
|
| > I dropped the hammer on the table and it smashed
|
| > I dropped the vase on the table and it smashed
|
| Exact same grammatical structure but if you switch the noun the
| way you parse the sentence changes. This is why machine learning
| with huge datasets wins in NLP, translation etc.
| schoen wrote:
| As you may know, the AI test based on these ambiguities is
| called the Winograd Schema Challenge:
|
| https://en.wikipedia.org/wiki/Winograd_Schema_Challenge
| eindiran wrote:
| There is also the GLUE benchmark, which includes ambiguity
| handling but also includes other NLU tasks:
| https://gluebenchmark.com/
| dariusj18 wrote:
| Also, the basis of a lot of humor is when the opposite of what
| you expect is what is intended.
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