A corpus-based analysis of the stylistic features of Chinese and American diplomatic discourse.

The study investigates the linguistic aspects of Chinese and American diplomatic discourse using Biber's theoretical underpinnings of multi-dimensional (MD) analysis. The corpus of the study comprises texts taken from the official websites of the Chinese and US governments from 2011 to 2020. The study results show that China's diplomatic discourse falls into the text type of learned exposition which includes informational expositions focused on conveying information. In contrast, the United States diplomatic discourse falls into the text type of "involved persuasion," which is persuasive and argumentative. Furthermore, the two-way ANOVA test reveals few distinctions between spoken and written diplomatic discourse from the same country. Furthermore, T-tests demonstrate that the diplomatic discourse of the two countries differs significantly in three dimensions. In addition, the study highlights that China's diplomatic discourse is informationally dense and context independent. In contrast, the United States diplomatic discourse is emotive and interactional, strongly dependent on context, and created within time restrictions. Finally, the study's findings contribute to a systematic knowledge of the genre aspects of diplomatic discourse and are helpful for more effective diplomatic discourse system creation.

Lexical Features of Economic Legal Policy and News in China Since the COVID-19 Outbreak.

Lexical features are influenced by different languages and genres. The study of lexical features in different genres of texts on the same topic is helpful to understand the universalities and peculiarities of languages. This study constructs a research on the lexical feature and word collocations of two self-build corpora (China's economic Legal Policy Corpus and English News Corpus during the COVID-19 pandemic), the methods of Quantitative Linguistics and context interpretation are adopted. It was found that: (1) the word length, word frequency, word cluster and high frequency word distribution in English economic news and Chinese economic legal policies are influenced by language and genre to some extent, and they conform to different functional image distribution; (2) during the COVID-19 pandemic, "development" has been the focus of China's economic legal policies and English news, the two have attached importance to economic recovery and taken a positive attitude toward it in different ways. These findings suggest that: (1) There are some universalities and peculiarities between English economic news and Chinese economic legal policies in the distribution of lexical feature; (2) there is a certain synchronization between laws and news, and both of them maintain a positive and objective attitude toward the economic development during the pandemic. This study carries out a macroscopic investigation on internal structure and external interpretation, which enriches the study on lexical features and cultural features of language and provides some references for relevant studies.

Implicit consequentiality bias in English: A corpus of 300+ verbs.

This study provides implicit verb consequentiality norms for a corpus of 305 English verbs, for which Ferstl et al. (Behavior Research Methods, 43, 124-135, 2011) previously provided implicit causality norms. An online sentence completion study was conducted, with data analyzed from 124 respondents who completed fragments such as "John liked Mary and so…". The resulting bias scores are presented in an Appendix, with more detail in supplementary material in the University of Sussex Research Data Repository (via https://doi.org/10.25377/sussex.c.5082122 ), where we also present lexical and semantic verb features: frequency, semantic class and emotional valence of the verbs. We compare our results with those of our study of implicit causality and with the few published studies of implicit consequentiality. As in our previous study, we also considered effects of gender and verb valence, which requires stable norms for a large number of verbs. The corpus will facilitate future studies in a range of areas, including psycholinguistics and social psychology, particularly those requiring parallel sentence completion norms for both causality and consequentiality.

Estimating the prevalence and diversity of words in written language.

Recently, a new crowd-sourced language metric has been introduced, entitled word prevalence, which estimates the proportion of the population that knows a given word. This measure has been shown to account for unique variance in large sets of lexical performance. This article aims to build on the work of Brysbaert et al. and Keuleers et al. by introducing new corpus-based metrics that estimate how likely a word is to be an active member of the natural language environment, and hence known by a larger subset of the general population. This metric is derived from an analysis of a newly collected corpus of over 25,000 fiction and non-fiction books and will be shown that it is capable of accounting for significantly more variance than past corpus-based measures.

Gender bias at scale: Evidence from the usage of personal names.

Recent research within the computational social sciences has shown that when computational models of lexical semantics are trained on standard natural-language corpora, they embody many of the implicit biases that are seen in human behavior (Caliskan, Bryson, & Narayanan, 2017). In the present study, we aimed to build on this work and demonstrate that there is a large and systematic bias in the use of personal names in the natural-language environment, such that male names are much more prevalent than female names. This bias holds over an analysis of billions of words of text, subcategorized into different genres within fiction novels, nonfiction books, and subtitles from television and film. Additionally, we showed that this bias holds across time, with more recent work displaying the same patterns as work published tens or hundreds of years previously. Finally, we showed that the main cause of the bias comes from male authors perpetuating the bias toward male names, with female authors showing a much smaller bias. This work demonstrates the potential of big-data analyses to shed light on large-scale trends in human behavior and to elucidate their causes.

Mining a Crowdsourced Dictionary to Understand Consistency and Preference in Word Meanings.

Big data approaches to psychology have become increasing popular (Jones, 2017). Two of the main developments of this line of research is the advent of distributional models of semantics (e.g., Landauer and Dumais, 1997), which learn the meaning of words from large text corpora, and the collection of mega datasets of human behavior (e.g., The English lexicon project; Balota et al., 2007). The current article combines these two approaches, with the goal being to understand the consistency and preference that people have for word meanings. This was accomplished by mining a large amount of data from an online, crowdsourced dictionary and analyzing this data with a distributional model. Overall, it was found that even for words that are not an active part of the language environment, there is a large amount of consistency in the word meanings that different people have. Additionally, it was demonstrated that users of a language have strong preferences for word meanings, such that definitions to words that do not conform to people's conceptions are rejected by a community of language users. The results of this article provides insights into the cultural evolution of word meanings, and sheds light on alternative methodologies that can be used to understand lexical behavior.