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Diachronic Interpretations of Word Order Parameter Cohesion

Diachronic Interpretations of Word Order Parameter Cohesion. John Whitman (NINJAL ・ Cornell) Yohei Ono (The Graduate University for Advanced Studies) DiGSXV 2013.8.1 University of Ottawa jbw@ninjal.ac.jp. 1. Basic idea.

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Diachronic Interpretations of Word Order Parameter Cohesion

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  1. Diachronic Interpretations of Word Order Parameter Cohesion John Whitman (NINJAL ・Cornell) Yohei Ono (The Graduate University for Advanced Studies) DiGSXV 2013.8.1 University of Ottawa jbw@ninjal.ac.jp

  2. 1. Basic idea A long tradition in the functional/typological literature attributes crosscategorialconsituent order generalizations on word order to a language change (Givon 1975, Aristar 1991). Suppose this idea is right.

  3. 1. Basic questions Two questions still remain: (i) Exactly what constituent order properties cohere? (ii) Why those properties?

  4. 1. Basic idea This paper sets out to investigate these questions based on a statistical investigation of latent interrelationships between typological parameters (“features”) in WALS (Dryer and Haspelmath 2011) (Ono et al 2013).

  5. 1.1. Roadmap • Cross-categorial word order generalizations (CWOGs) are statistical • CWOGs in a factor/cluster analysis of typological parameters • Significant CWOGs are limited to head-argument order • This is meaningful for understanding how syntactic change produces CWOGs

  6. 2.1 Cross-categorial word order generalizations (Greenberg 1963/6, Dryer 1999) Cross-categorial word order generalizations (CWOGs) are a subtype of implicational universal that relates the internal constituent order properties of distinct categories.

  7. 2.1 Cross-categorial word order generalizations Example: Greenberg’s (1963/6) Universal 3 • Universal 3. Languages with dominant VSO order are always prepositional. (Greenberg 1966: 78).

  8. 2.2 Cross-categorial word order generalizations are statistical We have much more data about constituent order typology than we did 50 years ago. Universal 3 is statistical. Greenberg himself (1963: 107) cites Papago(O'odham)as an exception to Universal 3. More counterexamples are provided by Payne (1986). Dryer in Haspelmath et al (2005) lists 6 languages which are postpositional with dominant VSO order (out of a total of 38 postpositional languages with dominant VO order).

  9. 2.2 Cross-categorial word order generalizations are statistical The same is true for more complex CWOGs that appeared to be exceptionless 50 years ago. • Universal 5. If a language has dominant SOV order and the genitive follows the governing noun, then the adjective likewise follows the noun.(Greenberg 1966: 79).

  10. 2.2 Cross-categorial word order generalizations are statistical Plank (2003) credits Dryer for the observation that Tigre counterexemplifies Universal #5. Tigre has OV, noun-genitive, and adjective-noun order. (2) Tigre NP internal order (Raz 1983: 95) a. Galab 't 'ətyopyalatətrakkab[hattenə'isdəgge] ta. Galabin Ethiopia which is found one small town ‘Galab is a small town which is found in Ethiopia.’ b. 'Azehattemə ’əl 'ət[həday 'adwa'aga] fararaw Now one day to wed party family guenon 3PL.went ‘One day they went out to the wedding party of the family of the guenon.’

  11. 2.2 Cross-categorial word order generalizations are statistical Even CWOGs whose exceptions are very, very token-rare give no evidence for being difficult to acquire. For example, (3) VO -> N Rel is counterexemplified by only 5 languages out of 705 VO languages in Dryer & Haspelmath (2011). 3 are Sinitic, 1 (Bai) heavily and 1 (Amis) possibly influenced by Chinese. Yet altogether, well over a billion speakers acquire this order. There is no evidence that this or other rare consitutent orders are difficult to acquire.

  12. 2.2 Cross-categorial word order generalizations are statistical CWOGs also have a peculiar theoretical status. Suppose that Complement – Head order is generated by leftward movement of the complement to Spec of a functional projection (Kayne 1994, Chomsky 1995): (4) FP[ XP YP[ Y tXP ]] It is not obvious why (or how) functional projections in completely distinct categories (e.g. VP and PP) should trigger the same movement.

  13. 2.2 Cross-categorial word order generalizations are statistical The original formal expression of CWOGs, the Head Parameter (Chomsky 1981) was a meta-constraint on d-structure. But that level of representation no longer exists in contemporary derivational theories.

  14. 2. Cross-categorial word order generalizations are statistical CWOGs are not the only kind of generalization bearing on word order. Whitman (2008) distinguishes 3 types of generalizations. • Cross-categorial generalizations (CWOGs) • Hierarchical generalizations • Derivational generalizations

  15. 2. Cross-categorial word order generalizations are statistical Hierarchical generalizations are the type of generalization targeted by Cartography (Rizzi 1997, Cinque 1999), Rizzi 2004 and many others). Insofar as heads and their specifiers high in the structure are spelled out on the left, hierarchical generalizations affect constituent order.

  16. 2. Cross-categorial word order generalizations are statistical Greenberg’s Universal 1 is a hierarchical generalization. • Universal 1. In declarative sentences with nominal subject and object, the dominant order is always one in which the subject precedes the object. (Greenberg 1966: 76).

  17. 2. Cross-categorial word order generalizations are statistical Derivational generalizations affect constituent order through constraints on possible derivations. Greenberg’s Universal 7 can be understood this way. • Universal 7. If in a language with dominant SOV order, there is no alternative basic order, or only OSV as the alternative, then all adverbial modifiers of the verb likewise precede the verb. (This is the rigid subtype of III.) (Greenberg 1966: 78)

  18. 2.2 Cross-categorial word order generalizations are statistical Suppose complement-head order is derived by movement of the complement to a higher spec. There are two scenarios: (5) NPSUBJ[NPOBJ [ V tOBJ ...]] (SOVX Mandic) (6) NPSUBJ[[... NPOBtV ...]VP [V...tVP]] (Japanese)

  19. 2.3 Harmony and disharmony Recently, Biberauer et al (2011) have proposed a more sophisticated constraint on constituent order “disharmonies”: situations where head-complement order differs across categories in a single language.

  20. 2.3 Harmony and disharmony Biberauer et al’s Final-over-Final Constraint (FOFC) rules out the case where a head-initial phrase α is immediately dominated by a head-final phrase ß, where α and ß are non-distinct in categorical features: ( 7)*ß α ß α γP

  21. 2.3 Harmony and disharmony As Biberauer et al point out, the FOFC can be understood as a derivational generalization, since it applies within a single derivation, as opposed to CWOGs, which must apply across distinct derivations. ( 7)*ß α ß α γP

  22. 2.3 Harmony and disharmony Even so, the FOFC is not secure. As van Riemsdijk(1990) shows, West Germanic “cricumpositions” have the constituency [PospositionalP[PrepositionalPPrep DP ] Postp] (cf. Djamouri et at 2013, to appear): (8) [PostP [PrePunterder Brücke] durch] under theDATbridgeDAT through ‘through under the bridge’

  23. 2.4 CWOGs: Summary • CWOGs have exceptions. They are statistical. • There is no evidence that exceptions to CWOGs (disharmonies) are hard to learn. • As such, CWOGs are not good candidates for components of UG. • Hierarchical and derivational generalizations account for many word order generalizations. They are good candidates for components of UG. • The functional/typological view of residual CWOGs is right. They are byproducts of syntactic change.

  24. 2.5 CWOGs from syntactic reanalysis Diachronic explanations CWOGs are proposed by Givon (1975, 1979) and are the focus of Aristar (1991).

  25. 2.5 CWOGs from syntactic reanalysis

  26. 2.5 CWOGs from syntactic reanalysis Partial List of Chinese Ps (Djamouri et al 2013) a. Prepositionb. Postpostion cháo‘facing’hòu‘behind; after’ cóng ‘from’ lái ‘for, during’ dāng(zhe) ‘at, facing’lǐ‘in’ dào‘to’ nèi‘inside, within’ duì‘toward’páng‘next to, at the side of’ duìyú ‘with respect’qián‘in front of; before’ gěi‘to; for’qiánhòu ‘around’ gēn‘with’ shàng‘on’ gēnjù ‘according to’ shàngxià ‘around, about’ guānyú ‘concerning’ wài‘outside, beyond’ lí‘from, away’xià‘under’ tì‘instead of, foryĭhòu‘after’ (temporal) wǎng‘in the direction of’yĭlái‘since, during’ wèi(le)‘for the sake of’yĭnèi‘inside, within’ xiàng‘in the direction of’yĭqián‘before, ago’ yán(zhe) ‘along’yĭshàng ‘above, over’ zài‘in, at’ yĭwàioutside, beyond’

  27. 2.5 CWOGs from syntactic reanalysis Reanalysis of Chinese relational nouns as Ps (Djamouri et al 2013) (9)三月之後(Guanzi管子 85·9/3, 1st c. BCE) [DP sānyuèzhīhòu]… three month genposteriority “After three months…” (10)閏當在十一月後(Hanshu漢書, 2nd c. CE) rùndāngzài[PostPshíyīyuèhòu] leap:month must be:at 11 month after ‘The leap month must occur after the eleventh month.’

  28. 2.6. CWOGs from reanalysis: summary Well-attested cases V > Aux, P, C; N > P, C Less clear cases v[noml] > vP (Claudi 1993, 1994 for Niger-Congo; Aldridge this conference for Austronesian)

  29. 3. A factor/cluster analysis of parameters in WALS online (Ono et al 2013) • Investigates interrelationships values of 225 parameters (“features”) for 236 languages in The World Atlas of Language Structures Online (Dryer & Haspelmath2011). • First applies a non-metric factor analysis called Hayashi’s Quantification Method III to identify latent relationships between WALS feature values • Next applies a standard clustering technique (Wall 1963) to the output of QMIII.

  30. 3.1 A factor/cluster analysis of feature values in WALS online (Ono et al 2013) B C

  31. 3.1 The full story (Ono et al 2013)

  32. 3.1 Quantification Method Ⅲ: Demonstrated for 2 dimensional space

  33. 3.1 QMⅢ applied to WALS feature values (2 dimensions)

  34. 3.1 Cluster analysis applied to the output of QMIII 3. NtayLlamL-phe’HL-koLFkətayL-muŋLFceLF-sayL. this thing-obj-top who-even know-vsm ‘This, anyone knows.’ (Jingphaw; Ohnishi 2012: 175)

  35. 3.1 Result: Salient clusters (red circles)

  36. 3.1 The highest order clusters again (Ono et al 2013) B C

  37. 3.2 Cluster A: Head-initial WO values

  38. 3.2 Cluster B (1): Head-final WO values

  39. Cluster B (2): Head-final WO values

  40. 3.2 Cluster A classification accuracies Feature Value Classification accuracy 37A [definite word distinct from demonstrative]: 63% 81A [SVO]: 92% 83A [VO]: 92% 84A [VOX]: Could not be evaluated, as 47% of languages lack data. 85A [Prepositions]: 92% 86A [Noun-Genitive]: 84% 90A [Noun-Relative clause]: 74% 90C [NRel dominant]: Could not be evaluated, as 41% of lgs. lack data. 94A [Initial subordinator word]: 81% 95A [VO and Prepositions]: 89% 96A [VO and NRel: 76% 97A [VO and NAdj]: 62% 144 [No NegSVO]: Could not be evaluated , as 70% of lgslack data. 144J [No SVNegO]: Could not be evaluated, as 68% of lgslack data. 144K [No SVONeg]: Could not be evaluated, as 68% of lgslack data.

  41. 3.2 Cluster B classification accuracies (1) Feature Value Classification accuracy 29A: [No subject person/number marking] 36% 40A: [No person marking] 38% 81A [SOV]: 92% 83A [OV]: 92% 85A [Postpositions]: 92% 86A [Genitive-Noun]: 84% 95A [OV and Postpositions]: 89% 97A [OV and NAdj]: 62%

  42. 3.2 Cluster B classification accuracies (2) Feature Value Classification accuracy 100A: [Neutral] 38% 102A: [No Person Marking] 38% 103A: [No Person Marking] 39% 112A: [Negative affix] 58% 126A: [‘When’ clauses] 42% 143E: [None] 60% 143F: [V-Neg] 62% 144A: [MorphNeg] 61% 144P: Could not be evaluated, as 58% of lgslack data. 144Q: Could not be evaluated, as 58% of lgslack data. 144R: Could not be evaluated, as 58% of lgslack data. 144S: Could not be evaluated, as 56% of lgslack data.

  43. 3.2 Cluster C • Contains by far the largest number of feature values (107) • Contains feature values in all subareas (phonology, morphology, lexicon) • Contains primarily unmarked values: 1A2 Consonant inventory: Moderately small 1A3 Consonant inventory: Average 17A1 Rhythm type: Trochaic 82A1 Order of subject and verb: SV

  44. 3.2 Cluster D • Residual values not in A-C

  45. 3.3 Assessment of the factor/cluster analysis • Constituent order features are completely dominant. • Other features often proposed for “whole” language classifications (e.g. head/dependent marking) are absent from the highest order clusters. • Highest order cluster: entirely head-initial values • Second order cluster: mostly head-final values. • Subset of constituent order features in A, B contains: V – O V- P C – TP (A only) V – Neg B only, weak) “Deranked” when clauses (B only, weak)

  46. 4. Significant CWOGs are limited to head-argument order 81A Order of subject, object, and verb92% 83A Order of object and verb92% 85A Order of adposition and NP92% 95A Order of object and verb and P and NP89% 86A Order of genitive and noun 84% 94A Initial subordinator word]: 81%

  47. 4. Significant CWOGs are limited to head-argument order • WOGs bearing on specifiers alone show up in C, the unmarked cluster. X82AOrder of Subject and Verb SV X88AOrder of Dem and N N-Dem

  48. 4. Significant CWOGs are limited to head-argument order • WOGs bearing on modifiers alone show up in C, the unmarked cluster, or in both A and B. X87A Order of N-Adj N-Adj(C) X97A Order of O-V and order of N-AdjN-Adj (A, B)

  49. 5. Diachronic interpretation • The most robust CWOGs are limited to head-complement orders. • These correspond to the well-known “relabeling”-type reanalyses(V > P, N > P, V/P/N > C etc.). • The link between VP internal order and NP internal order is limited to order of noun and genitive. • This is consistent with the hypothesis that reanalysis of nominalizations as verbal categories is responsible for CWOGs involving nominal and verbal projections.

  50. References • Aristar, Anthony R. 1991. On diachronic sources and synchronic patterns: An investigation into the origin of linguistic universals. Language 67: 1-33. • Biberauer, Theresa, Holmberg, Anders & Roberts, Ian 2012. A syntactic universal and its consequences. Linguistic Inquiry27: 195-236 • Chomsky, N. 1981. Lectures on Government and Binding, Dordrect: Foris. • Cinque, Guglielmo. 1999. Adverbs and Functional Heads. A Cross-LinguisticPerspective. Oxford: Oxford University Press. • Cysouw, M. 2005. Quantitative methods in typology. In Altmann, G., Köhler, R., and Piotrowski, R. (eds.), Quantitative Linguistik: eininternationalesHandbuch, 554–578. Berlin: Mouton de Gruyter. • Cysouw, M. 2007. New approaches to cluster analysis of typological indices. In ReinhardKöhler & Peter Grzbek (eds.), Exact Methods in the Study of Language and Text. (Quantitative Linguistics 62), 61-76. Berlin: Mouton de Gruyter. • Dryer, M. 1988. Universals of negative position. In Hammond, M., Moravcsik. E. and Wirth, J. (eds.), Studies in Syntactic Typology, 93‐124. Amsterdam: John Benjamins.

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