Data Analytics in Cognitive Linguistics

Methods and Insights

Dennis Tay, Molly Xie Pan, Dennis Tay, Molly Xie Pan

• Publisher: De Gruyter Mouton
• Year: 2022
• Print ISBN:
  9783111353463
  9783110687156
• Edition: 1
• eBook ISBN:
  9783110687347
• Topic: Languages & Linguistics
• Subtopic: Linguistics
• Index: Languages & Linguistics

No detailed description available for "Data Analytics in Cognitive Linguistics".

Lectal variation in Chinese analytic causative constructions: What trees can and cannot tell us

Xiaoyu Tian

Department of Linguistics, University of Leuven & Institute of Linguistics, Shanghai International Studies University

Weiwei Zhang

Department of Linguistics, University of Leuven & Institute of Linguistics, Shanghai International Studies University

Dirk Speelman

Department of Linguistics, University of Leuven

Acknowledgement: The authors are grateful to the Linguistic Data Consortium for providing the corpus of “Tagged Chinese Gigaword 2.0”. This project was supported by a China Scholarship Council grant to the first author (grant No.202006900017) and a Marie Skłodowska-Curie grant to the second author (European Union’s Horizon 2020 research and innovation programme, agreement No. 793920). The usual disclaimers apply.

1 Introduction

Years after its quantitative turn (cf. Janda 2013), Cognitive Linguistics experienced substantial methodological developments, which make it a suitable framework for exploratory studies that tap into large datasets and take into account multiple language-internal and language-external factors to model language variation (e.g. Colleman 2010; Zhang, Speelman, and Geeraerts 2011; Levshina, Geeraerts, and Speelman 2013a; Bernaisch, Gries, and Mukherjee 2014; Röthlisberger, Grafmiller, and Szmrecsanyi 2017). In addition to traditional hypothesis-testing regression modeling, more advanced statistical tools such as tree-based methods become more widely used to cope with the problems typically found in corpus data, such as data sparsity and collinearity (e.g. Tagliamonte and Baayen 2012; Bernaisch, Gries, and Mukherjee 2014; Szmrecsanyi et al. 2016). Recently, scholars have noticed the shortcomings of tree-based methods and proposed to combine them with regression models to yield more robust and interpretable results (cf. Strobl, Malley, and Tutz 2009; Gries 2019). In line with these methodological developments, this study explores the near-synonymous Chinese causative constructions from a cross-variety perspective using conditional random forests, conditional inference trees and multinomial logistic regression analysis.

According to Talmy (2000: Ch. 7), causation is a force-dynamic pattern that involves two main participants: the antagonist (labeled as the causer in this study) and the agonist (labeled as the causee in this study). The causer instigates a causing event or state, which affects the causee, who brings about the caused event. Linguistic means to express causation are called causatives or causative constructions. Based on the formal differences between the expressions of the cause and the effect, Comrie (1981) made a three-way distinction of causative constructions: morphological causatives, lexical causatives and analytic causatives. In morphological causatives, “the causative is related to the non-causative predicate by [productive] morphological means” (Comrie 1981: 167). When the relation between the cause and the effect is “handled lexically, rather than by any productive process”, lexical causatives are involved (Comrie 1981: 168). Analytic causatives refer to cases where “there are separate predicates expressing the notion of causation and the predicate of the effect” (Comrie 1981: 167). The current study focuses on the analytic causative constructions in Chinese.

Chinese analytic causative constructions involve se...

Table of contents

• Title Page
• Copyright
• Contents
• Data analytics in cognitive linguistics
• Mapping the landscape of exploratory and confirmatory data analysis in linguistics
• Time series analysis with python
• Structural equation modeling in R: A practical introduction for linguists
• Visualizing distributional semantics
• Lectal variation in Chinese analytic causative constructions: What trees can and cannot tell us
• Personification metaphors in Chinese video ads: Insights from data analytics
• The interaction between metaphor use and psychological states: A mix-method analysis of trauma talk in the Chinese context
• Prospecting for metaphors in a large text corpus: Combining unsupervised and supervised machine learning approaches
• Cognitive linguistics meets computational linguistics: Construction grammar, dialectology, and linguistic diversity
• What Cognitive Linguistics can learn from dialectology (and vice versa)
• Index