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A multiple-grammar model of speakers’ linguistic knowledge

  • Shoichi Iwasaki EMAIL logo
Published/Copyright: April 18, 2015

Abstract

By using the concept of ‘multiple grammars,’ this paper develops the view of an individual speaker’s cognitive organization of grammar. Although conversation, one type of spoken language environment, plays a crucial role in the emergence of grammar, for some speakers in a literate society, the written language environment may also contribute to developing a grammar. The two language environments are expected to provide unique incentives to shaping grammar differently as they diverge greatly in terms of media types (sound vs graph), constraints (online processing vs detachment), and purposes (interaction vs ideational formation), among others. At the same time, speakers may come in contact with and acquire additional sets of grammar for specific genres. Though the grammars acquired in different genre environments may be merged at the most abstract level, each grammar contains genre-specific formulaic expressions and grammatical resources with varying degrees of granularity. Speakers may conduct their routine linguistic activities in an informal conversation by employing reusable formulaic expressions of various types and rudimentary combinatory algorithms, but when they engage in more complex verbal tasks (politicians engaging in a debate, interviewees reconstructing past experiences), they may employ more abstract grammatical resources including those that were acquired from written language. The paper explores these suggestions by performing text and statistical analyses of several Japanese discourse samples.

Acknowledgments

I would like to thank the following people who gave me assistance during writing this paper: Mayumi Ajioka, Maggie Camp, Ewa Dąbrowska, Tetsuo Harada, Shimako Iwasaki, Michiko Kaneyasu, Yumiko Kawanishi, Chigusa Kurumada, Carol Lord, Tsuyoshi Ono, Yukinori Takubo, Sandra Thompson, Etsuko Yoshida, as well as the two anonymous reviewers of the earlier version of this paper. I’d also like to thank those institutions that provided me with opportunities to present the idea of a multiple-grammar model at its different stages of evolution: University of Tokyo (2005), Kyoto University (2006), National University of Singapore (2007), UC Santa Barbara (2007), National Institute of Japanese Language and Linguistics (2013), and Tokyo University of Foreign Studies (2013).

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Appendix A: Abbreviations

ACC

accusative

ASP

aspect

COM

comitative

COMP

complementizer

COP

copula

G

goal

GEN

genitive

GER

gerund

INF

infinitive

INS

instrumental

LOC

locative

MOD

modal

NEG

negative

NOM

nominative

PASS

passive

POT

potential

PP

pragmatic particle

PST

past

QT

quotative

SE

sentence extension

TOP

topic

Appendix B: Data for Figure 6(a) and 6(b)

The top 20 most frequent words used by Mr. H in his spoken and written discourses

SpokenWritten
1 nn (173/0)11 desu (36/0)1 no (450/73)11 hi (83/19)
2 ne (82/0)12 n (36/0)2 ni (280/41)12 koto (73/11)
3 te (74/121)13 to (33/181)3 ga (234/55)13 mo (69/24)
4 no (73/450)14 wa (33/233)4 wa (233/33)14 aru (67/8)
5 ta (68/105)15 yo (33/0)5 o (184/5)15 nihongo (67/0)
6 ga (55/234)16 moo (29/0)6 to (181/33)16 washa (60/0)
7 nee (50/0)17 u (28/23)7 de (165/50)17 iru (58/3)
8 de (50/165)18 ano (26/0)8 te (121/74)18 suru (56/4)
9 ni (41/280)19 desho (26/0)9 ta (105/68)19 nai (55/25)
10 ee (39/0)20 nanka (26/0)10 eego (102/0)20 hatsuon (55/0)

Appendix C: Data for Figure 9(a)–9(c)

The top 20 most frequent words in Mr. H’s conversation, CH, and Ono data

Mr. H’s conversationCall Home (20 conv.)Ono Data (13 conv.)
Word Types: 499Word Types: 3,343Word Types: 2,148
Word Tokens: 2,251Word Tokens: 39,882Word Tokens: 18,822
1 nn (173/2,818/516)1 nn (2,818)1 te (781)
2 ne (82/1,269/356)2 te (1,396)2 no (684)
3 te (74/1,396/781)3 no (1,396)3 nn (516)
4 no (73/1,396/684)4 ne (1,269)4 n (506)
5 ta (68/1,049/449)5 ta (1,049)5 -ta (449)
6 ga (55/640/316)6 soo (830)6 de (412)
7 nee (50/0/1)7 da (807)7 ne (356)
8 de (50/776/412)8 de (776)8 ni (350)
9 ni (41/762/350)9 ni (762)9 da (348)
10 ee (39/9/7)10 -nai (644)10 –nai (346)
11 desu (36/42/67)11 ga (640)11 wa (325)
12 n (36/561/506)12 wa (588)12 ga (316)
13 to (33/392/212)13 yo (569)13 soo (279)
14 wa (33/588/325)14 n (561)14 yo (276)
15 yo (33/569/276)15 na (507)15 tte (219)
16 moo (29/257/182)16 kara (462)16 shi (215)
17 –u (28/236/65)17 ano (422)17 na (215)
18 ano (26/236/44)18 a! (416)18 toka (214)
19 desho (26/120/45)19 ka (409)19 to (212)
20 nanka (26/298/158)20 mo (405)20 nn (203)
Mr. H’s conversationCall Home (20 conv.)Ono Data (13 conv.)
38 koto (11)40 o (169)35 o (105)
77 o (5)44 koto (144)59 koto (50)

Appendix D: Data for Figure 10(a) and 10(b)

The top 20 most frequent words in Mr. H’s written sample and newspaper editorials

H writtenNewspaper editorials
Total No. of Word Types: 925Total No. of Word Types: 3,113
Total No. of Word Tokens: 6,096Total No. of Word Tokens: 17,251
H writtenNewspaper editorials
1 no (450/1,214)1 no (1,214)
2 ni (280/663)2 o (692)
3 ga (234/448)3 ni (663)
4 wa (233/610)4 wa (610)
5 o (184/692)5 ta (465)
6 to (181/351)6 ga (448)
7 de (165/363)7 de (363)
8 te (121/358)8 te (358)
9 ta (105/465)9 to (351)
10 eego (102/0)‘English’10 shi (348)
11 shi (83/348)11 mo (182)
12 koto (73/125)12 nai (167)
13 mo (69/182)13 suru (164)
14 aru (67/116)14 da (147)
15 nihongo (67/0)‘Japanese’15 iru (135)
16 washa (60/0)‘speakers’16 koto (125)
17 iru (58/135)17 na (125)
18 suru (56/164)18 aru (116)
19 nai (55/167)19 -re (111)
20 hatsuonn (55/0) ‘pronunciation’20 sa (99)
Received: 2013-12-11
Revised: 2014-8-6
Accepted: 2014-12-19
Published Online: 2015-4-18
Published in Print: 2015-5-1

©2015 by De Gruyter Mouton

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