Applied Corpus Linguistics最新文献

This study describes the creation and analysis of a small multimodal corpus of British news articles about obesity, where tags were assigned to images in the articles using the automatic tagger Google Cloud Vision. In order to illustrate the potential for analysis of image tags, the corpus analysis tool WordSmith was used to identify differences between newspapers in the ways that obesity was framed. Three forms of analysis were carried out – the first simply compared keywords across the newspapers, the second examined key visual tags and their collocates associated with each newspaper, while the third incorporated a combined analysis of words and image tags. The three analyses produced complementary findings, indicating the value in using Google Cloud Vision in creating and analysing multimodal corpora. The paper ends by reflecting on the method undertaken, while considering how additional research could improve our understanding of image tagging.

Understanding the reception of public health messages in public-facing communications is of key importance to health agencies in managing crises, pandemics, and other health threats. Established public health communications strategies including self-efficacy messaging, fear appeals, and moralising messaging were all used during the Coronavirus pandemic. We explore the reception of public health messages to understand the efficacy of these established messaging strategies in the COVID-19 context. Taking a community-focussed approach, we combine a corpus linguistic analysis with methods of wider engagement, namely, a public survey and interactions with a Public Involvement Panel to analyse this type of real-world public health discourse.

Our findings indicate that effective health messaging content provides manageable instructions, which inspire public confidence that following the guidance is worthwhile. Messaging that appeals to the audience's morals or fears in order to provide a rationale for compliance can be polarising and divisive, producing a strongly negative emotional response from the public and potentially undermining social cohesion. Provenance of the messaging alongside text-external political factors also have an influence on messaging uptake. In addition, our findings highlight key differences in messaging uptake by audience age, which demonstrates the importance of tailored communications and the need to seek public feedback to test the efficacy of messaging with the relevant demographics. Our study illustrates the value of corpus linguistics to public health agencies and health communications professionals, and we share our recommendations for improving the public health messaging both in the context of the ongoing pandemic and for future novel and re-emerging infectious disease outbreaks.

The present study examines the relationship between the use of epistemic stance expressions (i.e., hedges and boosters) and task type, L2 proficiency, and individual authorial style in 1,773 essays representing three different kinds of tasks (complaint, letter, and opinion) written by 591 Finnish L2 English speakers on four different levels of proficiency (CEFR levels B1-C2). The results of the study show that the frequency of both hedges and boosters is mainly governed by task type, as the opinion tex contained a higher number of both hedges and boosters than the other tasks examined. Proficiency-related patterns were, nevertheless, also observed, as it was shown that in the complaint and opinion tasks, the frequency of both hedges and boosters tends to increase with proficiency, while in the letter task, the frequency of both ED types shows signs of decrease. Individual authorial style was shown to play a very limited role in the frequency of EDs in the data, but the results also suggest that the influence of authorial style may be greater with respect to boosters than it is with hedges.

This short ‘methods’ article compares results for six different corpus search techniques for identifying person-first language (e.g. person/people with obesity, person/people with mental illness) and identity-first language (e.g. obese person/people, mentally ill person/people) in a corpus. This distinction is relevant across a range of health contexts, including but not limited to obesity, diabetes, or mental illness. Consequently, there is considerable interest in corpus linguistics and beyond in identifying the frequency of such language in large corpora. However, there is no consensus regarding the specific corpus search techniques to be used for this purpose. This article therefore offers a relevant methodological contribution, based on a trial of six different search techniques. Results from each technique are compared with respect to four different parameters: raw frequency, proportional usage, number of types identified (a proxy for ‘recall’) and false positives (a proxy for ‘precision’). This comparison in turn provides a basis for recommendations for future corpus linguistic studies of person- and identity-first language. The corpus that we use for this trial is a 16.4 million word corpus with newspaper articles containing the word obesity or obese. However, the findings should be relevant to other kinds of identity where similar syntactic structures are at play for expressing identity-first and person-first language.

While much language teaching research has extolled the advantages of using corpora in the language learning classroom, uptake of corpora by language teachers remains low (Poole, 2020; Charles, 2020). Frankenberg-Garcia (2012a) reports that this is partly due to teachers having little time to prepare lessons with corpora given their busy teaching schedules. However, we also argue it is because teachers are not exposed to corpora sufficiently in pre-service teacher training programmes. This paper outlines a training framework for in-service EFL practitioners which will enable them to become corpus literate over a period of weeks. The framework utilises observational learning, and we gauge trainees’ corpus literacy development based on their reflections and experiences of teaching with corpora in class. Similar to previous corpus training research, teachers reported an overwhelmingly positive experience of learning to use a corpus for classroom materials design. Unlike previous trainings with corpora, teachers commented that using corpora did not require more time in lesson preparation when compared with traditional methods. Recommendations for future iterations of the training programme include the incorporation of additional corpora, tools and user interfaces.

Over the last three decades, extensive research has been devoted to EAP students’ use of corpora for academic writing. However, corpus use has usually been ascertained immediately post-course; data on long-term use is sparse and little attention has been paid to those who give up using corpora. This study investigates the extent of corpus non-use and students’ reasons for discontinuing the practice in the long term. It draws on data from two questionnaires: (1) immediate post-course (ImmPQ); (2) delayed post-course (DelPQ) completed a year later. Participants were 182 graduates who took a six-week course during which they built and consulted do-it-yourself corpora in their own field. Results from ImmPQ showed that most students (63%) used their corpus regularly (≥ 1/week), but one year later DelPQ revealed that regular use had decreased to 36%. Although 87% of respondents to ImmPQ stated their intention to use their corpus in the future, DelPQ reported a total of 37% of non-users. There were 86 mentions of reasons for non-use; the most prevalent were: not doing any academic writing (29%), the use of other tools (20%), time issues and corpus issues (10% each). It is argued that students’ scarcity of time is a possible underlying cause of much non-use and the study suggests some ways in which long-term corpus take-up could be increased.

Considerable energy has gone into designing lists of words that are salient in discourse domains of varying breadth. Over the past two decades, most efforts in designing and validating corpus-based frequency lists have focused on three areas: corpus compilation, item selection criteria, and coverage-based demonstrations of list robustness. As a result, modern corpora are now often much larger and better balanced; the application of additional dispersion statistics allows for better targeting of items with desired distributions; and contemporary lexical frequency lists are proving increasingly efficient, providing ever higher coverage of target texts or achieving such coverage with fewer words. However, despite these important advances, relatively minimal attention has been paid to word list reliability—the extent to which lists can be generalized to the wider discourse domain that has been represented by the corpora upon which they are based. This study begins to address this gap, demonstrating via two word list development case studies (one for Environmental Science and one for Applied Linguistics) that adding iterative reliability analysis—via methodological replication with corpora of increasing size and comparison of items on resulting lists—can be used to: 1) inform corpus design beyond what Biber (1991) terms “situational” parameters, allowing us to see whether corpora are adequately representative of lexical distributions in target discourse domains; and 2) provide valuable insight into the degree of generalizability of word lists we have developed.