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

Red = red

red dog = red + tail + barks + wet_nose

Why not?

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]

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.

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

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

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

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

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

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

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

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

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

Selective modification too narrow

Medin & Shoben

wooden spoon v. metal spoon

brass, silver, gold …coins? …railings?

Which pair is more similar?

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

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

Semantic Interaction Model

Noun rating (training input)

Adjective-noun rating (target)

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

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

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

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”

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

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)

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.

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.

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.

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..."

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

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.

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.

Review

• Fuzzy set model

• Selective Modification model

• Semantic Interaction model

• CARIN model

• Dual-process model of noun-noun combination

• knowledge and pragmatic factors