Corpora worth creating: A pilot study on telephone interpreting

This paper reports on the development and use of a corpus of interpreter-mediated phone calls to study features of telephone interpreting (TI) in healthcare settings. After a short introduction on TI and corpus-based studies of remote and on-site community interpreting (CI), the paper discusses ways of exploiting the corpus to analyse interpreters\u2019 translation and coordination activities over the phone. It first shows that, notwithstanding some limitations due to data originally collected for non-linguistic purposes, even a small and raw resource can contribute to exploratory analyses of TI, using a qualitative (Conversation Analysis) approach. It then illustrates how opportunities for more systematic research are opened up by corpus annotation. The paper finally reports on some preliminary insights about linguistic and interactional aspects characterizing this type of remote interpreting and makes a tentative comparison with two on-site CI corpora, thereby paving the way to more refined and quantitative investigations

Corpora worth creating: A pilot study on telephone interpreting

This paper reports on the development and use of a corpus of interpreter-mediated phone calls to study features of telephone interpreting (TI) in healthcare settings. After a short introduction on TI and corpus-based studies of remote and on-site community interpreting (CI), the paper discusses ways of exploiting the corpus to analyse interpreters’ translation and coordination activities over the phone. It first shows that, notwithstanding some limitations due to data originally collected for non-linguistic purposes, even a small and raw resource can contribute to exploratory analyses of TI, using a qualitative (Conversation Analysis) approach. It then illustrates how opportunities for more systematic research are opened up by corpus annotation. The paper finally reports on some preliminary insights about linguistic and interactional aspects characterizing this type of remote interpreting and makes a tentative comparison with two on-site CI corpora, thereby paving the way to more refined and quantitative investigations

Inter-Coder Agreement for Computational Linguistics

This article is a survey of methods for measuring agreement among corpus annotators. It exposes the mathematics and underlying assumptions of agreement coefficients, covering Krippendorff's alpha as well as Scott's pi and Cohen's kappa; discusses the use of coefficients in several annotation tasks; and argues that weighted, alpha-like coefficients, traditionally less used than kappa-like measures in computational linguistics, may be more appropriate for many corpus annotation tasks—but that their use makes the interpretation of the value of the coefficient even harder. </jats:p

Keywords of written reflection - a comparison between reflective and descriptive datasets

This study investigates reflection keywords by contrasting two datasets, one of reflective sentences and another of descriptive sentences. The log-likelihood statistic reveals several reflection keywords that are discussed in the context of a model for reflective writing. These keywords are seen as a useful building block for tools that can automatically analyse reflection in texts

Creating a frequency-based Turkish-English Loanword Cognates Word List (TELCWL)

This lexical study aims to establish a frequency-based Turkish-English Loanword Cognates Word List (TELCWL) to assist Turkish English learners’ improvement in English language learning and the corresponding pedagogical practice. A final list of 582 Turkish-English loan-based cognate word pairs was derived from the New General Service List (NGSL) and the Frequency Dictionary of Turkish (FDT). For pedagogical purposes, the TELCWL was divided into five sublists with different features of the cognates in spelling and pronunciation. The coverages of the TELCWL were particularly high in discipline and field-specific corpora on average compared to general service written (5%) and spoken corpora (3.5%), accounting for more than 7%. This result suggests that the TELCWL may be more beneficial for enhancing learners’ reading and writing ability; in addition, not only general Turkish English learners but also learners who need to improve their English language proficiency in specific disciplines can benefit from the TELCWL. Further pedagogical implications are made for English instructors regarding the employment of the TELCWL in English classrooms in Turkey

Leveraging professional wordlists for productive vocabulary knowledge

Productive knowledge of subject-specific vocabulary is essential for successful professional communication. This article puts forward the case for an innovative approach to course and materials design in English for Professional Purposes (EPP) that highlights the importance of careful analysis of the vocabulary of specific professional discourse. It argues that EPP courses would benefit from being informed by corpus-based analysis of vocabulary and collocational choices in texts used in professional contexts. The argument is supported by the results of the corpus-based analysis of the discourse in the professional context of the European Union institutions. The analysis was carried out using the 1-million-word English EU Discourse (EEUD) Corpus, which was created based on a target needs analysis. The present study contributes to knowledge in the field by establishing the first comprehensive EU word and collocation list, which comprises 405 word families and is complemented by collocational patterns specific to English EU discourse. The results underpin the article’s central argument that collocational information should be used to enrich professional wordlists as they reveal subject-specific patterns that are fundamental for productive vocabulary knowledge in efficient professional communication. The pedagogic applications of the word and collocation lists are also demonstrated. <br/

An overview of the features of chatbots in mental health: A scoping review

Background: Chatbots are systems that are able to converse and interact with human users using spoken, written, and visual languages. Chatbots have the potential to be useful tools for individuals with mental disorders, especially those who are reluctant to seek mental health advice due to stigmatization. While numerous studies have been conducted about using chatbots for mental health, there is a need to systematically bring this evidence together in order to inform mental health providers and potential users about the main features of chatbots and their potential uses, and to inform future research about the main gaps of the previous literature. Objective: We aimed to provide an overview of the features of chatbots used by individuals for their mental health as reported in the empirical literature. Methods: Seven bibliographic databases (Medline, Embase, PsycINFO, Cochrane Central Register of Controlled Trials, IEEE Xplore, ACM Digital Library, and Google Scholar) were used in our search. In addition, backward and forward reference list checking of the included studies and relevant reviews was conducted. Study selection and data extraction were carried out by two reviewers independently. Extracted data were synthesised using a narrative approach. Chatbots were classified according to their purposes, platforms, response generation, dialogue initiative, input and output modalities, embodiment, and targeted disorders. Results: Of 1039 citations retrieved, 53 unique studies were included in this review. Those studies assessed 41 different chatbots. Common uses of chatbots were: therapy (n = 17), training (n = 12), and screening (n = 10). Chatbots in most studies were rule-based (n = 49) and implemented in stand-alone software (n = 37). In 46 studies, chatbots controlled and led the conversations. While the most frequently used input modality was writing language only (n = 26), the most frequently used output modality was a combination of written, spoken and visual languages (n = 28). In the majority of studies, chatbots included virtual representations (n = 44). The most common focus of chatbots was depression (n = 16) or autism (n = 10). Conclusion: Research regarding chatbots in mental health is nascent. There are numerous chatbots that are used for various mental disorders and purposes. Healthcare providers should compare chatbots found in this review to help guide potential users to the most appropriate chatbot to support their mental health needs. More reviews are needed to summarise the evidence regarding the effectiveness and acceptability of chatbots in mental health

Sharing Knowledge, Transforming Societies: The Norhed Programme 2013-2020

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