A multivariate corpus analysis of locative inversion in Mandarin Chinese
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Yuting Li
Abstract
This study seeks to contribute to the understanding of the semantics and pragmatics associated with locative inversion (LI), a construction characterised by a preverbal locative phrase and a postverbal noun phrase. While previous studies have suggested that newness, definiteness, and heaviness are responsible for licensing locative inversion, few have examined the effect of one of these factors in conjunction with the other two. Many claims about locative inversion have not been quantitatively assessed. In response, this study presents a quantitative analysis of 899 instances including 562 LI and 337 uninverted constructions (non-LI) collected from the Center for Chinese Linguistics (CCL) Corpus, providing further empirical evidence for the effects of discourse information, the definiteness effect, and heaviness observed in the literature. Among other factors, discourse information shows a more pronounced effect. The result also introduces the novel finding of the effect of animacy on the choice of locative variants, a topic not yet discussed in existing studies. Additionally, an examination of verb semantics reveals varying preferences for locative variants among verbs. The finding that unergative verbs and non-passivized transitive verbs are found in locative inversion in Mandarin Chinese challenges the conventional belief that locative inversion serves as an unaccusative diagnostic.
Acknowledgements
The author thanks four reviewers and the editor for the constructive and helpful comments, and Judith Meinschaefer, Carolin Ulmer and Barbara Schirakowski for their insightful comments on an early version of this paper. This research was funded by the POSTDOC GRANT from the Department of Chinese Studies at the National University of Singapore.
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Data availability: The corpus dataset and supplementary materials used in this article are available at https://github.com/yutingli75/CLLT_corpus_dataset.git.
See Tables A.1–A.4.
Wang’s (2003) verb classes in locative inversion.
| Verb type | Verbs | |
|---|---|---|
| Monovalent verbs | 1. Movement verbs | 跳 tiào ‘jump’ 走 zǒu ‘walk’ 跑 pǎo ‘run’ 滚 gǔn ‘roll’ 转 zhuàn ‘rotate’ 飞 fēi ‘fly’ 撞 zhuàng ‘strike’ 抢 qiǎng ‘grab’ 落 luò ‘fall’ 赶 gǎn ‘rush’ 进 jìn ‘enter’ 来 lái ‘come’ 冲 chōng ‘dash’ 闯 chuǎng ‘intrude’ 窜 cuàn ‘flee’ |
| 2. (dis)appearance verbs | 现 xiàn ‘appear’ 露 lù ‘show’ 闪 shǎn ‘flash’ 显 xiǎn ‘appear’ 隐 yǐn ‘disappear’ 没 méi ‘disappear’ 起 qǐ ‘rise’ 钻 zuān ‘emerge’ | |
| 3. Float and swing verbs | 旋 xuán ‘spin’ 扬 yáng ‘float’ 飘 piāo ‘float’ 摇 yáo ‘swing’ 晃 huàng ‘shake’ 荡 dàng ‘swing’ 浮 fú ‘float’ | |
| 4. Stand and lie verbs | 立 lì ‘erect’ 站 zhàn ‘stand’ 坐 zuò ‘sit’ 卧 wò ‘lie’ 躺 tǎng ‘lie’ 睡 shuì ‘sleep’ 蹲 dūn ‘squat’ 歇 xiē ‘rest’ 跪 guì ‘kneel’ 盘 pán ‘hover’ 倒 dào ‘fall’ 爬 pá ‘climb, crawl’ 趴 pā ‘grovel’ 仰 yǎng ‘look up’ | |
| 5. Flow verbs | 流 liú ‘flow’ 涌 yǒng ‘gush’ 淌 tǎng ‘flow’ 渗 shèn ‘ooze’ 滚 gǔn ‘roll’ 泛 fàn ‘flood’ 溜 liū ‘slide’ | |
| 6. Diffusion verbs | 射 shè ‘spurt’ 透 tòu ‘permeate’ 喷 pēn ‘spray’ | |
| Bivalent verbs | 7. Occurrence verbs | 出 chū ‘exit’ 下 xià ‘descend’ 生 shēng ‘happen’ 冒 mào ‘appear’ 长 zhǎng ‘grow’ 产 chǎn ‘give birth to’ 降 jiàng ‘descend’ |
| 8. Putting verbs | 设 shè ‘display’ 堆 duī ‘pile’ 积 jī ‘store’ 安 ān ‘install’ 放 fàng ‘place’ 供 gōng ‘supply’ 排 pái ‘arrange’ 列 liè ‘list’ 置 zhì ‘place’ 摆 bǎi ‘display’ 贴 tiē ‘paste’ 摊 tān ‘spread’ 横 héng ‘place’ 竖 shù ‘erect’ 铺 pū ‘lay’ 埋 mái ‘bury’ 晾 liàng ‘hang’ 晒 shài ‘hang’ 敷 fū ‘spread’ 屯 tún ‘store’ 粘 zhān ‘stick’ | |
| 9. Hold verbs | 持 chí ‘hold’ 挟 xié ‘hold’ 执 zhí ‘grasp’ 抱 bào ‘hug’ 打 dǎ ‘bundle’ 扛 kāng ‘shoulder’ 拄 zhǔ ‘hold’ 拖 tuō ‘pull’ 捧 pěng ‘hold’ 掂 diān ‘take’ 托 tuō ‘hold’ 挺 tǐng ‘support’ 挎 kuà ‘carry’ 牵 qiān ‘lead’ 端 duān ‘hold’ 提 tí ‘lift’ 拈 niān ‘pick’ 引 yǐn ‘lead’ 擎 qíng ‘hold’ 掖 yē ‘tuck’ | |
| 10. Lean verbs | 迎 yíng ‘face’ 映 yìng ‘mirror’ 连 lián ‘join’ 接 jiē ‘connect’ 临 lín ‘face’ 邻 lín ‘adjacent’ 靠 kào ‘lean’ 依 yī ‘lean’ 傍 bàng ‘lean’ | |
| 11. Speak and sing verbs | 吹 chuī ‘blow’ 鸣 míng ‘ring’ 奏 zòu ‘play’ 念 niàn ‘talk’ 唱 chàng ‘sing’ 喊 hǎn ‘yell’ 敲 qiāo ‘knock’ 哼 hēng ‘hum’ 叨 dāo ‘talk’ 说 shuō ‘speak’ 诵 sòng ‘chant’ 讷 nè ‘mumble’ | |
| 12. Follow verbs | 牵 qiān ‘lead’ 带 dài ‘bring, take’ 引 yǐn ‘lead’ 跟 gēn ‘follow’ 随 suí ‘follow’ | |
| 13. Contain verbs | 怀 huái ‘bosom’ 含 hán ‘contain’ 衔 xián ‘hold’ 装 zhuāng ‘install’ 包 bāo ‘package’ 藏 cáng ‘hide’ 裹 guǒ ‘wrap’ 夹 jiā ‘clip’ 塞 sāi ‘plug’ 贮 zhù ‘store’ 囚 qiú ‘imprison’ 关 guān ‘close’ 盛 chéng ‘fill’ 杂容 záróng ‘contain’ 圈 quān ‘circle’ | |
| 14. Wear verbs | 戴 dài ‘wear’ 抹 mǒ ‘smear’ 佩 pèi ‘wear’ 披 pī ‘wrap’ 踏 tà ‘wear’ 别 bié ‘pin’ 著(着) zhuó ‘wear’ 服 fú ‘wear’ 衣 yī ‘wear’ 簪 zān ‘wear’ 蹬 dēng ‘wear’ | |
| 15. Plant verbs | 种 zhòng ‘plant’ 植 zhí ‘plant’ 栽 zāi ‘plant’ 插 chā ‘insert’ 养 yǎng ‘grow’ | |
| 16. Remove verbs | 死 sǐ ‘die’ 杀 shā ‘kill’ 除 chú ‘remove’ 丢 diū ‘throw’ 撤 chè ‘withdraw’ 脱 tuō ‘fall’ 弃 qì ‘abandon’ | |
| Trivalent verbs | 17. Cooking verbs | 烧 shāo ‘burn’ 点 diǎn ‘light’ 燃 rán ‘burn’ 煮 zhǔ ‘boil’ 烹 pēng ‘cook’ 焚 fén ‘burn’ 煨 wēi ‘stew’ 烤 kǎo ‘grill’ 蒸 zhēng ‘steam’ 温 wēn ‘warm’ 烘 hōng ‘bake’ |
| 18. Writing verbs | 画 huà ‘draw’ 题 tí ‘write’ 写 xiě ‘write’ 刻 kè ‘carve’ 印 yìn ‘imprint’ 嵌 qiàn ‘embed’ 镌 juān ‘engrave’ 雕 diāo ‘carve’ 绣 xiù ‘embroider’ 刺 cì ‘stab’ 书 shū ‘write’ 描 miáo ‘depict’ | |
| 19. Hang verbs | 悬 xuán ‘hang’ 挂 guà ‘hang’ 垂 chuí ‘droop’ 张 zhāng ‘stretch’ 搭 dā ‘build’ 叼 diāo ‘hold’ 吊 diào ‘hang’ 挑 tiāo ‘shoulder’ | |
| 20. Build verbs | 造 zào ‘build’ 起 qǐ ‘build’ 建 jiàn ‘build’ 塑 sù ‘build’ 盖 gài ‘build’ 筑 zhù ‘build’ 架 jià ‘put up’ 搭 dā ‘build’ 支 zhī ‘build’ 砌 qì ‘build’ 做 zuò ‘do’ | |
| 21. Cover verbs | 覆 fù ‘cover’ 盖 gài ‘cover’ 罩 zhào ‘cover’ 套 tào ‘harness’ 笼 lóng ‘coop’ 扣 kòu ‘cover’ | |
| 22. Tie verbs | 系 xì ‘tie’ 扎 zhā ‘bundle’ 围 wéi ‘enclose’ 缚 fù ‘bind’ 拴 shuān ‘tie’ 绾 wǎn ‘tie’ 缆 lǎn ‘rope’ 挽 wǎn ‘coil’ |
Statistics on the modern Chinese corpus (Zhan et al. 2019).
| Data before 1949 | Bytes | Percentage | Data after 1949 | Bytes | Percentage |
|---|---|---|---|---|---|
| Literature | 14,052,591 | 92.15 % | Newspaper | 839,973,730 | 71.45 % |
| Drama | 1,197,572 | 7.85 % | Translation | 90,046,147 | 7.66 % |
| Literature | 85,241,162 | 7.25 % | |||
| Online corpora | 54,680,142 | 4.65 % | |||
| Practical writing | 48,286,885 | 4.11 % | |||
| TV movie | 21,359,547 | 1.82 % | |||
| Academic literature | 20,655,712 | 1.76 % | |||
| Historical biography | 8,799,888 | 0.75 % | |||
| Cross-talk sketch comedy | 3,480,086 | 0.30 % | |||
| Spoken language | 3,081,723 | 0.26 % | |||
| Total | 15,250,163 | 100 % | Total | 1,175,605,022 | 100 % |
The comparison of the model with and without verb class.
| Model | npar | AIC | BIC | logLik | deviance | χ2 | Df | Pr (>χ2) |
|---|---|---|---|---|---|---|---|---|
| Model (without verb) | 10 | 463.51 | 509.21 | -221.76 | 443.51 | |||
| Model (with verb) | 11 | 386.78 | 437.05 | -182.39 | 364.78 | 78.728 | 1 | <2.2e-16*** |
Confusion matrix showing predictions compared to the actual defaults.
| Prediction/Reference | Uninverted sentence | Locative inversion | Total | Accuracya |
|---|---|---|---|---|
| Uninverted sentence | 60 | 11 | 71 | |
| Locative inversion | 12 | 103 | 115 | |
| Overall | 87.63 % |
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aBy default, any individual in the test data set with a probability of default greater than 0.5 is predicted to default (namely locative inversion in this case). Using this threshold, one can generates a confusion matrix that shows the predictions compared to the actual defaults.
The variance inflation factor (VIF) of 7 independent variables in the model.
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