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Can we devise a new measure of syntactic complexity for language acquisition?

Can we devise a new measure of syntactic complexity for language acquisition? Duna ú jv á ros, March 18, 2004 Marijan Palmović Laboratory for Psycholinguistic Research University of Zagreb Marijan.Palmovic@public.srce.hr http://public.srce.hr/labpolin/.

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Can we devise a new measure of syntactic complexity for language acquisition?

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  1. Can we devise a new measure of syntactic complexity for language acquisition? Dunaújváros, March 18, 2004 Marijan Palmović Laboratory for Psycholinguistic Research University of Zagreb Marijan.Palmovic@public.srce.hr http://public.srce.hr/labpolin/

  2. Mean Lenght of Utterance – MLU – a traditional measure • Simple and good measure for assessing syntactic development • but • there are problems in morphologically rich languages • -not valid crosslinguistically In Croatian: - Does not discriminate between children of various developmental rate?

  3. Vjeran Antonija Marina Croatian corpus (CHILDES): MLU in morphemes

  4. Vjeran Antonija Marina Croatian corpus (CHILDES): MLU in words

  5. Various measures for assessing lexical development 1 Type/token ratio (TTR) • Generally: • Sensible to the size of the file: • - larger the file, smaller the TTR • -- questionable application in morphologically rich languages • Specifically, for Croatian: • Not an automatized measure • - does not reflect lexical complexity • -- ratio always around 1

  6. Various measures for assessing lexical development 2 • Number of different words in a standard number of utterances • - longer the sentences (and therefore MLU), higher the NDW. NDW – Number of Different Words Fixing the number of tokens, not utterances VOCD: a smarter TTR - Accounts for the file size -- takes randomly chosen samples of text

  7. RRG • Role and Reference Grammar • Van Valin, LaPolla, 1997, Syntax

  8. The overall organization of the RRG (Van Valin, LaPolla)

  9. Aktionsart: • State • Achievement • Accomplishment • Activity • Active accomplishment + Causative counterparts -causative state, causative achievement… The ice melted. Hot water melted the ice. Accomplishmentcausative accomplishment

  10. Verb class Logical structure State predicate’ (x) or (x,y) Activity do’ (x, [predicate’ (x) or (x,y)]) Achievement INGR predicate’ (x) or (x,y) Accomplishment BECOME predicate’ (x) or (x,y) Lexical representation for the basic Aktionsart classes: (Van Valin, LaPolla, 1997) John ate the fish. do’ (John, [eat’ (John, fish)]) Macroroles actor undergoer

  11. SENTENCE CLAUSE CORE NUCLEUS PRED PERIPHERY ARG ARG ADV NP V NP John ate the fish yesterday U A do’ (John, [eat’ (John, fish)])

  12. Antonija (CHILDES) Table representing the number of different verb classes - the first step

  13. Antonija, 1;9: *ANT: Ja idem tebi. %Eng: I am going to you. *ANT: Ja ću ovo držati. %Eng: I will hold this. *ANT: Neću tebi dati olovku. %Eng: I will not give you the pencil.

  14. Complex sentences as represented in RRG

  15. nuclear clausal cosubordination coordination strongest weakest connection connection Type type Interclausal Relations Hierarchy - IRH

  16. CL COO 9 Attach points to different juncture and nexus types… …to modify the scale obtained from points given for logical form variety CL SUB 8 CL COSUB 7 CORE COO 6 CORE SUB 5 CORE COSUB 4 NUC COO 3 NUC SUB 2 NUC COSUB 1

  17. An example: Strongest connection: nuclear cosubordination English examples: John pushed open the door. Vince wiped the table clean. Prediction: -should be acquired first!

  18. But in Croatian, these sentences are translated differently: John je gurnuo vrata i otvorio ih. Vinko je očistio stol.

  19. The question about the acquisition of complex sentences is reduced to the question about the child’s ability to learn IRH with the stronger connection ocurring first. • A child can deduce other grammatical properties of complex sentences, for example, the ones regarding the operators of tense and aspect. • Child starts to use clause linkage constructions before mastering the total range of constructions and operators possible in simple sentences. • e.g. Hocu piti cajeka. ‘I want to drink tea.’ • Modify our scale with IRH (add 1 - 9 points)

  20. verb + verb in infinitive in the purposive construction Examples: Antonija, 1;7: two word sentences, e.g.Hocem ovo. ‘Want this.’ 5+0 Antonija, 1;9 core cosubordination:Idemo pjevati! ‘Let’s sing!’ 4+5 Antonija, 1;10: clausal coordination: Nis-am pjev-a-la, nego ja s-am pak-a-la. Be_not-1SG:PRES sing-5-PART:F but I be-1SG:PRES cry-5-PART:F 'I was not singing, but I was crying.' 4+9

  21. Comparison with the MLU Antonija, 1;7: two word sentences – 5: MLU (words) = 1,57 MLU (morphemes) = 2,29 Antonija, 1;9 core cosubordination – 9: MLU (words) = 2,43 MLU (morphemes) = 3,53 Antonija, 1;10: clausal coordination – 13: MLU (words) = 2,19 MLU (morphemes) = 3,43

  22. Problems: • It is not easy to apply – it is not an automatic measure • Selection of verb classes can be problematic due to the imperfective – perfective oppositions of verbs • Questionable predictivity regarding the complex sentences: children like to add sentences with conjection i ‘and’.

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