Combining concepts

1. Combining concepts Cognitive Science week 9

2. compositionality • Fuzzy set model • Selective Modification model • Semantic Interaction model • CARIN model • Dual-process model of noun-noun combination • knowledge and pragmatic factors

3. This is too simple to work • Dog = tail + barks + wet_nose • Red = red • red dog = red + tail + barks + wet_nose • Why not?

4. 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]

5. 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.

6. 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

7. 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

8. 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

9. Selective Modification model • Represent concepts as frames • a set of slots with potential values • each slot is weighted (‘salience’) • Apple1.0 COLOR red 25 • green 5 • brown • 0.5 SHAPE round 15 • square • 0.3 TEXTURE smooth 25 • bumpy

10. Selective Modification model • Goodness measured by adding up matches (and taking away mismatches) • Object (X, COLOR = brown, SHAPE = round, TEXTURE = smooth) • Apple1.0 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

11. Selective Modification model • Combination selects slots • disambiguates potential values • increases weight of selected slot • Apple1.0 COLOR red 25 • green 5 • brown • 0.5 SHAPE round 15 • square • 0.3 TEXTURE smooth 25 • bumpy Red

12. Selective Modification model • Combination selects slots • disambiguates potential values • increases weight of selected slot • Apple2.0 COLOR red 30 • green • brown • 0.5 SHAPE round 15 • square • 0.3 TEXTURE smooth 25 • bumpy Red

13. Selective Modification model • Combination selects slots • disambiguates potential values • increases weight of selected slot • Apple1.0 COLOR red 25 • green 5 • brown • 0.5 SHAPE round 15 • square • 0.3 TEXTURE smooth 25 • bumpy Brown

14. Object (X, COLOR = brown, SHAPE = round, TEXTURE = smooth) • Combination selects slots • disambiguates potential values • increases weight of selected slot • Apple2.0 COLOR red • green • brown 30 • 0.5 SHAPE round 15 • square • 0.3 TEXTURE smooth 25 • bumpy Brown 1.0 * 30 0.5 * 15 0.3 * 25 = 45

15. Selective modification too narrow • Medin & Shoben • wooden spoon v. metal spoon • brass, silver, gold …coins? …railings? • Which pair is more similar?

16. Limits of Medin & Shoben • 1. What about lexicalisation? • wooden spoon familiar, stored • 2. What about ambiguity? • gold1 – made of the substance gold • gold2 – painted a gold colour • 3. Lack of an explicit model

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

18. Semantic Interaction Model Some mouldy peas Adjective-noun rating (target) Noun rating (training input) Some peas

19. 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

20. 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

21. 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

22. 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”

23. 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

24. 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)

25. 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.

26. 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.

27. 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.

28. 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..."

29. 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

30. 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.

31. 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.

32. Review • Fuzzy set model • Selective Modification model • Semantic Interaction model • CARIN model • Dual-process model of noun-noun combination • knowledge and pragmatic factors