Combining concepts Cognitive Science week 9 compositionality Fuzzy

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 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 frames a 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 slots disambiguates potential values increases weight of selected slot Red Apple 1. 0 COLOR red 25 green 5 brown 0. 5 SHAPE round 15 square 0. 3 TEXTURE smooth 25 bumpy

Selective Modification model Combination selects slots disambiguates potential values increases weight of selected slot Red Apple 2. 0 COLOR red 30 green brown 0. 5 SHAPE round 15 square 0. 3 TEXTURE smooth 25 bumpy

Selective Modification model Combination selects slots disambiguates potential values increases weight of selected slot Brown Apple 1. 0 COLOR red 25 green 5 brown 0. 5 SHAPE round 15 square 0. 3 TEXTURE smooth 25 bumpy

Object (X, COLOR = brown, SHAPE = round, TEXTURE = smooth) Combination selects slots disambiguates potential values increases weight of selected slot Brown Apple 2. 0 COLOR red green brown 30 0. 5 SHAPE round 15 square 0. 3 TEXTURE smooth 25 bumpy 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. 1. What about lexicalisation? 2. wooden spoon familiar, stored 3. 2. What about ambiguity? 4. gold 1 – made of the substance gold 5. gold 2 – painted a gold colour 6. 3. Lack of an explicit model

Semantic Interaction Model Dunbar, Kempen & Maessen (1993) Property ratings nouns adjective-noun some peas some mouldy peas Effect of the adjective = the difference Effect not the same for different nouns

Semantic Interaction Model Adjective-noun rating (target) Noun rating (training input)

Semantic Interaction model Results for adjective mouldy Training items broccoli cabbage bananas peas . 013. 007. 001. 027 Test item carrots . 011 Mean error for carrots with random weights (10 runs) = 0. 49

Noun-noun combination peanut butter made of peanuts mountain hut in the mountains zebra 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) relational the 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 move Similarities - 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