Semantics
What do words or sentences mean
Meanings are public property: the same meaning be grasped by more than one person and by people at different times. Heavily related to representation
# Implications
- If this argument is correct, then the content of our thoughts partly depends on what is in the world we live in
- Content determines object
- What about indexical thoughts? Thoughts that depend on context like “you”, “me”, “there”, “here”
- Concept of meaning rests on two unchallenged assumptions
- Understanding a word (knowing its intension) was just a matter of being in a certain psychological state
- meaning of a term determines its extension
- extension → actual physical manifestation / what it is
- proving these are false →
Twin Earth Argument
- extension of the term is not a function of the psychological state of the speaker by itself
- water on twin earth vs earth in 1750; both oscars think it to be the same thing
- extension of the term is not a function of the psychological state of the speaker by itself
# Sociolinguistic hypothesis
- division of linguistic labour
- some people wear gold rings
- some people tell the difference between gold and non-gold
- not everyone needs to tell the difference between gold and non-gold, rely on the judgement of experts
- formal: “every linguistic community exemplifies the sort of division of linguistic labour just described; that is, it possesses at least some terms whose associated “criteria” a re known only to a subset of the speakers who acquire the terms, and whose use by the other speakers depends upon a structured cooperation between them and the speakers in the relevant subsets”
- two theories → if “water” means $H_2O$ in $W_1$ and “water” means $XYZ$ in $W_2$
- world-relative but constant in meaning, ‘water’ means the same thing in both world, but water is $H_2O$ in $W_1$ and water is $XYZ$ in $W_2$
- water is $H_2O$ in all worlds, but ‘water’ doesn’t mean the same thing in $W_1$ and $W_2$
- Twin-earth argument implies 2nd one is true
- Rigidity → if a designator in a particular sentence refers to the same individual in every possible w in W
- Water is rigid in (2)
- Once we have discovered the nature of water, nothing counts as a possible world in which water doesn’t have nature
- i.e. one we have discovered that water (in the actual world) is h2o, nothing counts as a possible world in which water isn’t h2o
- Water at another time or in another place or even in another possible world has to bear the relation same to our “water” in order to be water
- Types of statements
- epistemically necessary → rationally unrevisable
- metaphysically necessary → true in all possible worlds
# Lexical Development
- Mental lexicon: mental dictionary of word knowledge (how it sounds, grammar, definition, etc.)
- Word: symbol that refers to something
- Symbol: stands for something without being a part of that something
- Context-bound word: things tied to particular contexts (word use is more specific than actual meaning)
- Nominals: names for things
- Natural partitions hypothesis: the physical world makes obvious the things that take nouns as labels, whereas the meanings that verbs encode have to be figured out from hearing the verb in use
- Relational relativity hypothesis: possibility that verb meanings will vary from language to language (linguistic work showing that noun meanings are more similar across languages than are verb meanings)
- Word extension: to what extent is a word valid?
- Underextensions: using words in a more restricted fashion
- Overextensions: using words in a more broad fashion (for related study, see Naigles & Gelman 1995 study, results showed that overextensions are mistakes, they don’t indicate incorrect understanding of the words)
- Protowords (also known as phonetically consistent forms – PCFs)
- Phonetically consistent: the child uses the same word every time.
- Things that help with accurate word extension:
- Taxonomic extension: words to things are actually taxonomies (they are of the same category)
- Word spurt: see Choi & Gopnik (1995)
- Types of language use, two ends of a continuum
- Referential language style: more object labels
- Expressive language style: relatively fewer object object labels and more personal/social words
- Mapping problem: how do we know what the new word refers to?
- Fast mapping: initial hypothesis about word meaning
- Lexical principles/lexical constraints: guides that limit possible interpretations of new words
- Whole-object assumption: words refer to whole objects
- Assumption of mutual exclusivity: different words refer to different kinds of things. No category overlap
- Lexical gaps: Sometimes things are not a one-to-one match – your language may not have a lexical item for something
- Age at which children learn early words (first 50-100) can vary a lot due to
- Environmental Factors
- Language experience and input
- Socioeconomic status (SES)
- Birth order
- Individual Factors
- Processing speed
- Phonological memory
- Personality and temperament
- Environmental Factors
# Deep Learning Semantics
# Images
Semantics in convolutional neural networks
Hidden units often correlate semantically-meaningful concepts.
Inceptionism: what about, instead of weights, use backpropagation to take gradient with respect to $x_i$. i.e., show me what you think a banana looks like
Style Transfer: loss function matches deep latent representation of content image $C$:
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Difference between $z_i^{(m)}$ for deepest $m$ between $x_i$ and $C$
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Intuition, deep layers $z_i^{(m)}$ capture the semantics/concepts in an image, invariant to actual style
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Adversarial Examples: imperceptible noise that changes label/prediction.
- Potentially dangerous! We could repaint a stop sign and fool self-driving cars
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It can learn bad correlations (e.g. correlating grass with cows so when it sees a cow by a beach it has no idea what it is)
- Related: does the prediction change real-world outcomes?
- i.e., does the doctor actually care?
- Does “not trying to overfit” mean we perform badly on some groups?
- If you have 99% “Group A” in your dataset, model can do well on average by only focusing on Group A
- Treat the other 1% as outliers
- Doing well at test-time might mean ignoring outliers and minorities
- See also: bias bug