Types of Noun Phrases Referential vs. Quantified NPs

1. Types of Noun PhrasesReferential vs. Quantified NPs Inherently referring noun phrasespick out individuals in the world. e.g. John, Mary, the President, the department chair Non-referring noun phrasesdo not pick out an individual (or individuals) in the world. We will call these ‘quantified NPs’ or ‘operators’ e.g. No bear, every boy, all the students, each boy, who, two rabbits, some teacher, etc.

2. A Paradigm with a Missing Cell 1) [No talk show host]i believes that Oprah admires himk 2) [No talk show host]i believes that Oprah admires himi 3) [No talk show host]i admires himk 4) *[No talk show host]i admires himi The phenomenon is the same for referential NP, e.g., ‘Geraldo’ Example (4) cannot mean that no talk show host admires himself; it must mean that no talk show host admires some other male

3. Two Accounts of Principle B 1. Geraldo believes that Oprah admires himk 2. Geraldoi believes that Oprah admires himi 3. Geraldoi admires himk 4. *Geraldoi admires himi 5. [No talk show host]i believes that Oprah admires himk 6. [No talk show host]i believes that Oprah admires himi 7. [No talk show host]i admires himk 8. *[No talk show host]i admires himi Chomsky: Principle B applies to pronouns which are c-commanded by referential NPs and to pronouns which have operators as antecedents (i.e. 1-8) Reinhart: Principle B applies only to pronouns which are c-commanded by an operator (i.e. 5-8). Example (4) is ruled out by pragmatic Rule I (Info strength)

4. The Experimental Findings Chien and Wexler (1990 Language Acquisition) Methodology: Picture-judgment task Results: First, children ‘appear’ to violate Principle B in sentences like ‘Mama Bear is touching her’, allowing the prohibited meaning, i.e. Mama Bear is touching herself about 50% of the time However, the same children do not violate Principle B when the antecedent of the pronoun is a quantified NP such as ‘every bear’. E.g., Every bear is touching her is accepted only 15% of the time in a situation where every bear touches herself, but no bear touches another salient female who is depicted in the picture (say Goldilocks).

5. Examples of Pictures Used. The reflexives make sure children allow this kind of interpretation Main Question:can children reject the picture on the right -->

6. Main Question:can children reject the picture on the right -->

7. G1: under 4yrsG2: 4-5yrsG3: 5-6yrsG4: 6-7yrsA: Adults

8. Look at G3 for largest difference

9. Summary of Chien and Wexler’s Findings In general, the youngest children didn’t perform well. It may be that the task is too hard for them, at least for some of the test sentences. Putting the youngest children (Groups 1 and 2) aside, however, children do pretty well with reflexives. They reject the mismatch sentence/picture pairs at high rates. Children also perform well with pronouns with a quantified NP as antecedent by 5-6 years-old (Group 3), but these children still accept coreference for pronouns with a referential NP as antecedent.

10. Picture Tasks Experiments using pictures can give us an idea about what’s going on in children’s grammars, although these experiments tend to underestimate children’s knowledge, as compared to experiments using the Truth Value Judgment task However, large numbers of subjects can be run, because the experiment is quick to carry out, though children don’t enjoy it. So, how would the results of Chien and Wexler’s experiment stand up -- if we switch to a TVJ task?

11. A Target Picture

12. What Isn’t Shown in a Picture? A story with ‘events’ taking place in real time. The target sentence is a possible outcomewhich satisfies the condition of plausible dissent but events took a different turn, so the actual outcome makes the sentence false Plausible denial is also unmet for QNPs Not every bear is touching her -- in fact, none of them are.

13. What Isn’t Shown in a Picture? Possible Outcome:Mama Bear touches GoldilocksEvents take a turn such that Mama Bear doesn’t touch Goldilocks, but touches herself

14. The TVJ task Background: Every troll dried so-and-so Assertion: Every troll dried a salient female character: ArielPossible Outcome: Every troll dried Ariel. Actual Outcomes: Only one troll dried Ariel Every troll dried herself NB: there are other Background/Assertion pairs

15. Test Sentence “Every troll dried her” The characters are introduced

16. Theme : A Swimming Story The 3 trolls are planning to go to the beach. They take towels, & a watermelon -- for a picnic

17. The Plot Unfolds The Trolls are swimming in the deep blue sea, when who along comes Ariel the Mermaid.

18. The Plot Unfolds The trolls invite Ariel to share the watermelon with them -- once they are all dried off. Ariel’s hair & tail are very wet.

19. Potential Antecedent for the Pronoun Ariel accepts, but needs to dry off. “Can you trolls help. My hair is very wet, and so is my tail.”

20. The Possible Outcome “Sure, we can help. We’ll go get our towels.”

21. Possible Outcome Troll 1: “I have a big towel. I’ll dry your hair, and then I’ll get dry too. I’m still wet.

22. Possible Outcome Troll 1: ”Here you go Ariel. Now your hair is dry.”

23. Condition of Falsification Troll 2: “Your tail is still wet. Oh look. I only brought a small towel. It’s not big enough to dry us both. And I’m wet too.”

24. Possible Outcome becomes Untenable Troll 3: “I can’t help either. Your wet tail willsoak my little towel, and I need it to dry off.”

25. The Actual Outcome: & Reminder Troll 1: “Now I feel better” <troll dries self>Troll 2: “Me too.” Troll 3: “I’m getting all dry too.”

26. The Story Ends Let’s have watermelon together now. (The towels are still next to the Trolls.)

27. The Linguistic Antecedent “That was a story about Ariel and some girl trolls. And I know one thing that happened.Every troll dried her.”

28. The Truth Value Judgment Child: “No.” Kermit: “No? What really happened?”Child: “Only one troll dried her.”

29. Does the Truth Value Judgment Task Help? Yes and No. No: Using the Truth Value Judgment Task, children distinguish between pronouns with quantified NP antecedents and referential NP antecedents, permittinglocal coreference in with referential NPs as antecedents. Yes: The pattern is obtained for children at least one year younger. Thornton and Wexler (1999)Subjects: 19 children aged 4;0 to 5;1 (mean age = 4;8) Every reindeer brushed him 08% acceptanceBert brushed him 58% acceptance

30. Parameter Setting:Null Subjects

31. Null Subjects • Child English • Eat cookie. • Hyams (1986) • English children have an Italian setting of null-subject parameter • Trigger for change: expletive subjects • Valian (1991) • Usage of English children is different from Italian children (proportion) • Wang (1992) • Usage of English children is different from Chinese children (null objects)

32. Parameter Setting:Complex Predicates (Snyder 2001)

33. Complex Predicates English Spanish • John painted the house red resultative   • Mary picked the book up verb-particle   • Fred made Jeff leave make-causative   • Fred saw Jeff leave perceptual report   • Alice sent Sue the letter double object   • Alice sent the letter to Sue dative   • Bob put the book on the table put-locative  

34. N-N Compounding • English: frog man (various meanings)French: homme grenouille (fixed meaning) • English: banana cup wine glass party chair blood lady

35. Compounding Parameter Resultatives Productive N-N Compounds American Sign Language   Austrooasiatic (Khmer)   Finno-Ugric   Germanic (German, English)   Japanese-Korean   Sino-Tibetan (Mandarin)   Tai (Thai)   Basque   Afroasiatic (Arabic, Hebrew)   Austronesian (Javanese)   Bantu (Lingala)   Romance (French, Spanish)   Slavic (Russian, Serbo-Croatian)  

36. Compounding Parameter Resultatives Productive N-N Compounds American Sign Language   Austrooasiatic (Khmer)   Finno-Ugric   Germanic (German, English)   Japanese-Korean   Sino-Tibetan (Mandarin)   Tai (Thai)   Basque   Afroasiatic (Arabic, Hebrew)   Austronesian (Javanese)   Bantu (Lingala)   Romance (French, Spanish)   Slavic (Russian, Serbo-Croatian)   correlation is not bidirectional

37. Developmental Evidence • Complex predicate properties argued to appear as a group in English children’s spontaneous speech (Stromswold & Snyder 1997) • Appearance of N-N compounding is good predictor of appearance of verb particle constructions and other complex predicate constructions - even after partialing out contributions of • Age of reaching MLU 2.5 • Production of lexical N-N compounds • Production of adjective-noun combinations • Correlations are remarkably good

38. Language Change:Learning about Verbs

39. Classes of Verbs • Verbs with syntax like pour • dribble, drip, spill, shake, spin, spew, slop, etc. • Verbs with syntax like fill • cover, decorate, bandage, blanket, soak, drench, adorn, etc. • Verbs with syntax like load • stuff, cram, jam, spray, sow, heap, spread, rub, dab, plaster, etc. manner-of-motion change-of-state manner-of-motion & change-of-state

40. Learning Syntax from Semantics VP FigureFrame Manner-of-motion V NP PP ground figure GroundFrame VP Change-of-state V NP PP ground figure Linking Rules SEMANTICS SYNTAX

41. Assumption: linking generalizations are universal • Shared by competing accounts of learning verb syntax & semantics

42. Evidence • Semantics --> Syntax (Gropen et al., 1991, etc.) • Teach child meaning of novel verb, e.g. this is moaking • Elicit sentences using that verb • Syntax --> Semantics (Naigles et al. 1992; Gleitman et al.) • Show child multiple scenes, use syntax to draw attention • Adults: verb-guessing task, show effects of scenes, syntax, semantics.

43. Language Change Theoretical Approaches

44. Language Change • How to take input and reach a new grammar • Error signal • Failure to analyze input • Failure to predict input • Cues • Syntactic • Non-syntactic • How do any of these deal with problem of overgeneralization?

45. Triggers

46. Gibson & Wexler (1994) • Triggering Learning Algorithm • Learner starts with random set of parameter values • For each sentence, attempts to parse sentence using current settings • If parse fails using current settings, change one parameter value and attempt re-parsing • If re-parsing succeeds, change grammar to new parameter setting B + - + Si A -

47. Gibson & Wexler (1994) • Triggering Learning Algorithm • Learner starts with random set of parameter values • For each sentence, attempts to parse sentence using current settings • If parse fails using current settings, change one parameter value and attempt re-parsing • If re-parsing succeeds, change grammar to new parameter setting B + - Single Value Constraint + Si A Greediness Constraint -

48. Gibson & Wexler (1994) • For an extremely simple 2-parameter space, the learning task is easy - any starting point, any destination • Triggers do not really exist in this model VO VOS SVO OV SOV OVS SV VS

49. Gibson & Wexler (1994) • Extending the space to 3-parameters • There are non-adjacent grammars • There are local maxima, where current grammar and all neighbors fail VO VOS SVO +V2 OV SOV OVS -V2 SV VS

50. Gibson & Wexler (1994) • Extending the space to 3-parameters • There are non-adjacent grammars • There are local maxima, where current grammar and all neighbors fail String:Adv S V O VO VOS 8 SVO +V2 OV SOV OVS -V2 SV VS