combining concepts
DESCRIPTION
Combining concepts. Cognitive Science week 9. compositionality. Fuzzy set model Selective Modification model Semantic Interaction model CARIN model Dual-process model of noun-noun combination knowledge and pragmatic factors. This is too simple to work. Dog = tail + barks + wet_nose - PowerPoint PPT PresentationTRANSCRIPT
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Combining concepts
Cognitive Science week 9
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compositionality
• Fuzzy set model• Selective Modification model• Semantic Interaction model• CARIN model• Dual-process model of noun-noun
combination• knowledge and pragmatic factors
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This is too simple to work
Dog = tail + barks + wet_nose
Red = red
red dog = red + tail + barks + wet_nose
Why not?
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What does red modify: the coat of the dog, its nose?
What colour is red?
red brick, red wine, red pillar box
Compounds
red lurcher“sandy fawn red lurcher” [http://www.doglost.co.uk/forum.asp?
ID=9757]
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Red is an intersective adjective
Extensionally, simple set intersection almost works (apart from the problems above)
Skilful – set intersection simply won’t work
Betty is a skilful ballerina, but she’s useless at rugby.
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Fuzzy set theory
Instead of True (=1) or False (=0)
shades of gradable truth [0, 1]
Eg. A showjumper is a jockey = 0.7
Use a rule to combine these
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Red jockey
Take some object
Let’s rate it as a jockey = 0.7
as a red thing = 0.8
The rule is ‘min’, take the minimum
As a red jockey, it should be 0.7
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Conjunction effect
He would typically be rated as a better instance of “red jockey”
than of “red” or “jockey”
Another example, a brown apple
This is contrary to the min rule
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Selective Modification model
Represent concepts as framesa set of slots with potential values
each slot is weighted (‘salience’)
Apple 1.0 COLOR red 25
green 5
brown
0.5 SHAPE round 15
square
0.3 TEXTURE smooth 25
bumpy
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Selective Modification model
Goodness measured by adding up matches (and taking away mismatches)Object (X, COLOR = brown, SHAPE = round, TEXTURE = smooth)
Apple 1.0 COLOR red 25
green 5
brown
0.5 SHAPE round 15
square
0.3 TEXTURE smooth 25
bumpy
1.0 * 0
0.5 * 15
0.3 * 25 = 15
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Selective Modification model
Combination selects slotsdisambiguates potential values
increases weight of selected slot
Apple 1.0 COLOR red 25
green 5
brown
0.5 SHAPE round 15
square
0.3 TEXTURE smooth 25
bumpy
Red
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Selective Modification model
Combination selects slotsdisambiguates potential values
increases weight of selected slot
Apple 2.0 COLOR red 30
green
brown
0.5 SHAPE round 15
square
0.3 TEXTURE smooth 25
bumpy
Red
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Selective Modification model
Combination selects slotsdisambiguates potential values
increases weight of selected slot
Apple 1.0 COLOR red 25
green 5
brown
0.5 SHAPE round 15
square
0.3 TEXTURE smooth 25
bumpy
Brown
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Combination selects slotsdisambiguates potential values
increases weight of selected slot
Apple 2.0 COLOR red
green
brown 30
0.5 SHAPE round 15
square
0.3 TEXTURE smooth 25
bumpy
Brown
Object (X, COLOR = brown, SHAPE = round, TEXTURE = smooth)
1.0 * 30
0.5 * 15
0.3 * 25 = 45
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Selective modification too narrow
Medin & Shoben
wooden spoon v. metal spoon
brass, silver, gold …coins? …railings?
Which pair is more similar?
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Limits of Medin & Shoben
1. What about lexicalisation?wooden spoon familiar, stored
2. What about ambiguity?gold1 – made of the substance goldgold2 – painted a gold colour
3. Lack of an explicit model
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Semantic Interaction Model
Dunbar, Kempen & Maessen (1993)
Property ratingsnouns some peasadjective-noun some mouldy peas
Effect of the adjective = the difference
Effect not the same for different nouns
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Semantic Interaction Model
Noun rating (training input)
Adjective-noun rating (target)
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Semantic Interaction model
Results for adjective mouldy
Training items broccoli .013
cabbage .007
bananas .001
peas .027
Test item carrots .011
Mean error for carrots with random weights (10 runs) = 0.49
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Noun-noun combination
peanut butter butter made of peanuts
mountain hut hut in the mountains
zebra bag bag with zebra pattern
Property v. relational interpretations
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CARIN model
Gagne & Shoben (1997)
Past patterns affect interpretation(cf. statistical models of disambiguation)
People interpret faster if the relation is one that has often been used with this modifier
Eg. football scarf, football hat football flag
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CARIN model
Created a corpus of novel NN combinations
Judged interpretation for each NN
Counted frequency of different kinds of interpretation for each N
Used frequency to predict:Timed judgement “does this NN make
sense”
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Dual process model (Wisniewski, 1997)
relationalthe modifier occupies a slot in a scenario drawn from the conceptual
representation of the head
property (and hybrid)Two-stage process
1. Compare: areas of similarity, & so difference.Differences - candidate for the property to moveSimilarities - aspect to land the property on
2. The property transferred is elaborated. NN combinations are largely self-contained, a function largely of
"knowledge in the constituent concepts themselves" (1997, p. 174)discourse context may influence
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Wisniewski's evidence includes participant definitions for novel combinations presented in isolation:
property mapping as well as thematic interpretations (Wisniewski, 1996, Experiment 1)
property mapping is more likely if Ns are similar (Wisniewski , 1996, Experiment 2)
• novel combinations• null contexts "listeners have little trouble comprehending them"
(Wisniewski, 1998, p. 177)
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In real-world lexical innovation there is an intended meaning
Conjecture The need to convey an intended meaning, rather
than only the ability to construct a plausible interpretation, is key to understanding NN combination in English. NN combination is primarily something the speaker does with the hearer in mind, rather than the converse.
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Pragmatics - Relevance
Sperber & Wilson (1986)
Principle of Relevance presumption that acts of ostensive communication are optimally relevant.
Optimal relevance
1. The level of contextual effect achievable by a stimulus is never less than enough to make the stimulus worthwhile for the hearer to process.
2. The level of effort required is never more than needed to achieve these effects.
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Pragmatics - Relevance
Speaker chooses expression that requires least processing effort to convey intended meaning.
Consequently, first interpretation recovered (consistent with the belief that the speaker intended it) will be the intended interpretation.
If first interpretation not the correct one, then
speaker should have chosen a different expression, for example by adding explicit information.
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Clark and Clark (1979)Denominal verbs - "contextuals"
Tom can houdini his way out of almost any scrape
Sense can vary infinitely according to the mutual knowledge of the speaker and hearer
Any mutually known property of Houdini, if speaker:
"... has good reason to believe... that on this occasion the listener can readily compute [the intended meaning] ... uniquely... on the basis of their mutual knowledge..."
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Pragmatic approaches emphasise cooperative and coordinated activity by both speaker and hearer.
Self-containment approach emphasises NN combination as a problem for the listener.
On pragmatic account, notion of an interpretation in isolation from any context is defective
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Prediction:
readers presented with novel stimuli in isolation will experience difficulty:
They cannot make the presumption of optimal relevance, since they have no evidence of intentionality;
They therefore have no basis for differentiating the intended interpretation from any conceivable interpretation.
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A simple experiment: can participants interpret a novel NN in isolation?
Key finding:
Participants were typically unable to provide the correct interpretation.
In addition, they knew they didn’t know.
See Dunbar (2006) for details.
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Review
• Fuzzy set model
• Selective Modification model
• Semantic Interaction model
• CARIN model
• Dual-process model of noun-noun combination
• knowledge and pragmatic factors