Applied Corpus Linguistics最新文献

There is growing evidence that corpus-based computational tools are useful in identifying changes in speech that appear to accompany cognitive decline in Alzheimer's disease (AD). It has long been known that semantic coherence of speech is altered in AD, but only recently have computational tools been developed that allow for cohesion indices to be computed in an automated fashion on larger data sets. To that end, this study examined semantic coherence in persons with AD and healthy controls. Speech transcripts from 81 individuals with probable AD (Mage = 72.7 years, SD = 8.80, 70.4% female) and 61 healthy controls (Mage = 63.9 years, SD = 8.52, 62.3% female) from DementiaBank were analyzed. Machine learning analyses of coherence were conducted, and models evaluated for classification accuracy (i.e., AD vs controls) as well as ROC-AUC. Relationships between coherence indices and MMSE performance were also quantified. Though no significant group differences emerged in local semantic coherence among adjacent words, persons with AD produced less globally coherent speech relative to healthy controls. Furthermore, global coherence indices predicted AD diagnoses with accuracy between 75% and 78% and were significantly associated with MMSE scores. These findings suggest that automated measures of global coherence can distinguish individuals with AD from healthy controls, which may point to eventual diagnostic utility in clinical settings.

The importance of language to changing public behaviours is acknowledged in crisis situations such as the COVID-19 pandemic. A key means of achieving these changes is through the use of directive speech acts, yet this area is currently under-researched. This study investigates the use of directives in the 2020 COVID-19 briefings of four leaders of English-speaking nations, Jacinda Adern, Boris Johnson, Scott Morrison, and Nicola Sturgeon. We developed a classification system including 16 directive types and used this to compare directive use across these four leaders, examining directness and forcefulness of directive use. The analysis finds Sturgeon to be the most prolific directive user and also to have the highest reliance on imperatives. Johnson, meanwhile, has a preference for directives involving modal verbs, particularly with first- and second-person pronouns. In contrast, Ardern and Morrison show a higher use of indirect directives, normally thought to be a less effective strategy. While Ardern often combines this strategy with judicious use of imperatives, this is not seen in Morrison's COVID-19 briefings. These findings tend to confirm earlier, more impressionistic evaluations of the communication styles of these leaders but also suggest other avenues for research on directive use. We conclude with implications for political crisis communication and analysis of directives in crisis communication.

This study investigates the construction and (re)negotiation of the identity boundaries in the context of a white nationalist online forum. Using over three million words of data, a corpus linguistic approach is combined with elements of critical discourse analysis, namely social actor (van Leeuwen, 1996) and transitivity (Halliday and Matthiessen, 2004) analyses, to examine the positioning of social actors in relation to one another. The data shows that, despite an assumption amongst some scholars of a united and ideologically coherent in-group of extremists, forum members often disagree on the nature and boundaries of both their racial (white) and ideological (white nationalist) identities. This calls into question the value of the `in-group' concept as we currently understand it. Instead, the `in-group' in the far-right context should be seen as slippery and unfixed, comprising multiple overlapping but distinct identities.

Communication has played a critical role during the initial response to the COVID-19 pandemic, and communicators have had a particularly difficult task in persuading different types of audience to comply with ever-changing regulations. Local government organisations play a crucial role in recontextualising the national messaging for a local audience and encouraging the public to comply with regulations.

This paper investigates local government organisations’ (henceforth LGOs) engagement strategies in COVID-related posts on social media. In collaboration with LGOs in England, we examined their communication strategies on Twitter and Facebook during the second UK national lockdown of the COVID-19 pandemic in November-December 2020. Using methods from corpus-assisted discourse studies, the paper analyses the occurrence and functions of selected interactive engagement markers, in this case personal pronouns, questions and hashtags. We find that such linguistic features function to encourage engagement by (a) helping to foster relatedness through ambiguity; (b) creating autonomy-supporting communication; and (c) making messages ‘stand out’.

Based on our corpus analysis, we discuss the initial response of the participating councils to our findings and outline future directions including the integration of multimodal approaches to studying the role of localised social media in national crisis management. We argue for more attention to be paid to the many local communicators who play an invaluable role in encouraging the public to comply with national measures in times of crisis.

This article provides a comparative analysis of how frontline workers were constructed by the UK media prior to and during the COVID-19 pandemic in the UK. Both the News on the Web Corpus and the Coronavirus Corpus, as monitor corpora of web-based new articles, were utilised to identify changes in both the frequency and use of the word front*line from 2010 to 2021. Findings show that, following the outbreak of COVID-19, constructions of frontline work were more frequently associated with medical professions and became more figurative in nature. Our findings provide a counterpoint to claims that the COVID-19 pandemic led to an increased awareness of the critical nature of many types of ‘low-skilled’ work not previously recognised as essential. The study also extends previous research which has traced changes in language and its deployment during the COVID-19 pandemic.

With multimodal visual texts becoming increasingly important, especially online, there is a need for an effective tool to analyze these texts. In this paper, we present the Multimodal Corpus Analysis Tool which uses sets of categories, such as dominant colors and object location, to analyze a corpus of online advertisements. The 12 categories are based on both multimodal theory and advertising theory, and the tool produces frequency counts of codes and likelihood of co-occurrence of two codes using observed-to-expected ratio. We illustrate the use of the tool through three case studies. First, examining the left and right positioning of verbal content and images in horizontal adverts, we find that there is a general preference for language left – picture right adverts, a finding potentially questioning currently accepted theories of information value in multimodal texts. Second, examining color schemes in adverts, we find that most use of dominant colors reflects accepted meanings in advice on multimodal text design, except for blue which is associated with storge love. For brightness, colorfulness and contrast, there are differences between adverts for everyday products and adverts for services and expensive products. Third, focusing on the call to action phrases in the adverts, there are differences in the use of the two most common forms (shop now and learn more) in adverts for products and services. These cases illustrate the potential benefits of developing and using multimodal corpus tools.

Marking errors in L2 learner performance, though useful in both a didactic and academic sense, is a challenging process, one usually performed manually when involving learner corpora. This is because errors are largely latent phenomena whose manual identification and description involve a significant degree of judgment on the side of human annotators. The purpose of the paper is to discuss and demonstrate the implications of the two stages of the decision-making process that is manual error coding, error location and error description, for measuring inter-annotator agreement as a marker of quality of annotation. The crux of the study is in the proposal that inter-annotator agreement on error location and on error description should be considered and reported separately rather than, as is common, together as a single measurement. The case study, grounded in a high-stakes exam context and typified using an established error taxonomy, demonstrates the method behind the proposal and showcases its usefulness in real-world settings.

Beyond the context of cross-cultural pragmatics, it has been claimed that race and Rasse are not equivalent terms. The current paper seeks to establish the first known program of study to shed light on the extent to which race and Rasse differ. Corpora of US and German newspapers are used in this paper to sample mainstream race talk in the respective countries, and top collocates are analyzed along with a selection of examples. What is seen is that, while the historical context of slavery and the civil rights movement in the US and Nazism in Germany do seem to be reflected in the respective corpora, it is not so clear that the underlying meaning of race and Rasse are as different as some have claimed.