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The present study uses private correspondence to investigate the use of embedded inversion on both sides of the Atlantic as an illustration of the spread of spoken/conversational features through writing. The paper discusses the use of embedded inversion in Irish English (IrE) and briefly compares its occurrence in other varieties of English during the 19th century. The data come from CORIECOR, the Corpus of Irish English Correspondence, which contains emigrant letters written to and by Irish emigrants from 1760 to 1940. The letters were sent mainly between Ireland and other countries such as the United States, Canada, Great Britain, New Zealand, Australia, and Argentina, and therefore provide an empirical base for studies of historical change. By using corpus linguistics techniques, we analyze possible connections between American English and IrE. The occurrence of embedded inversion in CORIECOR is compared with other historical corpora such as the Corpus of Historical American English, which provides data from one of the main destinations of Irish emigrants. [ABSTRACT FROM AUTHOR]

Within a broadly Thomistic frame, this paper shows how simple apophaticism in the theology of Pseudo‐Dionysius the Areopagite is the more fitting mode of knowing the triune God, beyond the use of all divine names. Specifically, we will proceed using the work of Father Bernard Lonergan, SJ on theological fittingness. After setting forth Father Lonergan's understanding of fittingness, the paper will proceed through Dionysius's cataphatic names, apophatic names, and apophatic silence. The cataphatic and apophatic names, while true, useful, and fittingly said of God, will be shown to be imperfectly applied to God. As such, their fittingness pales in comparison to the simple silence of the intellect orientated towards God, despite such pure apophaticism's dissimilarity to normal human intellectual operations. [ABSTRACT FROM AUTHOR]

Research examining teachers’ cognition of lexical items has flourished in the past few years. However, most of these studies focused on individual words rather than formulaic sequences. The present study aims to fill this gap through eliciting teachers’ evaluation of one type of formulaic sequences: phrasal verbs (PVs). The aim is to examine factors (frequency, opacity, and learners’ receptive and productive knowledge) that might predict teachers’ evaluation of the difficulty and usefulness of 100 PV senses. For that purpose, a survey was administered to 174 teachers. Results of generalized mixed-effects modelling showed that teachers’ assessment of both the usefulness and difficulty of PVs was predicted by sense-specific corpus frequency and receptive knowledge of these PV senses. Moreover, difficulty ratings were additionally affected by opacity and productive knowledge of PVs. We discuss these findings in relation to previous research on L2 PV knowledge and implications for L2 teaching practice. [ABSTRACT FROM AUTHOR]

Nominal technical terms (NTTs), as crucial builders for disciplinary knowledge, can cause difficulties for students. However, previous studies have rarely associated NTTs with disciplinary knowledge construction. Apart from that, scholars of English for Specific Purposes (ESP) have mainly focused on making wordlists for one specific discipline based on corpora rather than on meanings. Moreover, the current categorization of technical terms cannot reveal their role in constructing disciplinary knowledge. Against this backdrop, we carried out a corpus-based study to classify NTTs in secondary school biology textbooks and to unveil the knowledge constructed by different types of those NTTs. As a result, we found that NTTs in those textbooks could fall into five major categories: Thing, Activity, Semiotic, Place, and Time. We also found intra-disciplinary differences in NTT distributions. The lexicogrammatical analysis indicates that the five types of NTTs can construct different knowledge. With the findings, we put forward implications for teaching biology in secondary schools. [ABSTRACT FROM AUTHOR]

The article focuses on the German and Hungarian present and future tenses and the Hungarian present+majd construction in the function of future time reference. Using a corpus of press reports of business, network and weather, a contrastive analysis is made of which factors determine the use of the grammatical means of expressing the future time and how. In particular, frequency, co-occurrence with temporal and modal adverbials, the semantic properties of full verb lexemes and use in subordinate clauses after communicative verbs are analysed. The linguistic data and tendencies are compared with the results of corpus analysis for language of immediacy. [ABSTRACT FROM AUTHOR]

Keen observers of Kenyan English usage will agree that idiomatic expressions such as put into consideration, rather than take into consideration, are certainly common in daily usage. The author of this paper set out to establish if that was indeed the case by having a sample of 122 respondents, all fourth-year university students, to choose between put and take. 77% of them chose put, which suggests that the expression put into consideration is indeed quite familiar to Kenyan English speakers. It was tested alongside another 19 idiomatic expressions. 17 out of the 20 were found to be familiar, though to varying degrees. But this familiarity was not reflected in corpus data from the International Corpus of English and the Corpus of Global Webbased English, where they were found to be rare. The same corpus data showed that this familiarity of the 20 expressions to Kenyan English speakers did not mean that they used them more frequently than their Standard International English counterparts. For example, the data showed that the "less familiar" take into consideration had more tokens in the Kenyan English components of the two corpora than the "more familiar" put into consideration. Nevertheless, the paper concludes that so-called Kenyan English idioms can still be claimed to be typical of Kenyan English, since they are practically absent from e. g. British English, its colonial ancestor. [ABSTRACT FROM AUTHOR]

Copyright of Basic & Clinical Andrology is the property of BioMed Central and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study argues that corpus analysis has yet to incorporate probabilistic reasoning into the measurement of dispersion. This proposal continues a recent observation that some commonly used dispersion measures were impaired by an extreme sensitivity to the random noise characterizing any real-world data set. This article will show that this could have been avoided if (a) the underlying metaphor for ‘corpus dispersion’ had been more carefully considered and (b) the measures had been derived from an appropriate probability mass distribution. The text then proposes a computationally efficient dispersion measure developed from a Poisson model of an unbiased word dispersion. Unlike several current measures, this measure is shown to give a score that is meaningful at all word frequencies while being more sensitive to real dispersional bias and less sensitive to noise. [ABSTRACT FROM AUTHOR]

Emotions have played a major part in the conversation, as they express context to the conversation. Text or words in conversation contain contextual and lexical meanings. In recent times, obtaining emotion from the text has been an attractive area of research. With the emergence of machine learning (ML) algorithms and hardware to aid the ML method, identifying emotion from the text with ML provides significant and promising solutions. The main objective of Textual Emotion Analysis (TEA) is to analyze and extract the user’s emotional states in the text. Many different Complex Systems and Deep Learning (DL) algorithms have been fast-paced developed and proved their effectiveness in several fields including audio, image, and natural language processing (NLP). This has moved researchers away from the classical ML to DL for their academic research work. This study develops a new Corpus Linguistics and Data-Driven Deep Learning for Textual Emotion Analysis (CLD3L-TEA) technique. The CLD3L-TEA technique mainly investigates the distinct types of emotions that endure in the social media text. In the CLD3L-TEA model, the raw data can be pre-processed in distinct ways. Next, a multi-weighted TF–IDF model is used to generate feature vectors. For the identification of emotions, the CLD3L-TEA technique applied a gated recurrent unit (GRU). At last, the hyperparameter range of the GRU model is executed by the Fractal Harris Hawks Optimization (HHO) model. The experimental validation of the CLD3L-TEA technique on a benchmark dataset illustrates the supremacy of this technique over recent approaches. [ABSTRACT FROM AUTHOR]

This paper discusses the use of zero and that as connectors for object clauses in eighteenth-century English. Previous approaches have shown that the use of zero increased in late Middle English and became well-established in the second half of the sixteenth century, especially in speech-based text-types. Our own research, based on the evidence of The Parsed Corpus of Early English Correspondence, has shown that in the mid-seventeenth century zero was well beyond the mid-range stage of lexical diffusion (73%) with the five high frequency verbs to know, to think, to say, to tell and to hope. Chronologically, it has been shown that progress of the zero innovation was thwarted in the eighteenth century, which has been attributed to the effect of prescriptivism. In this paper, we intend to throw light on this issue by tracking the courses of zero and that during the eighteenth century in both usage and precept. With this purpose we rely on two sources of information. On the one hand, a precept corpus containing a selection of grammars published in Britain in 1681–1800. Usage, on the other, has been surveyed in light of the evidence of The Corpus of Early English Correspondence Extension Sampler. Our aim is to assess the existence of a correlation between usage and precept in the expression of object clauses in this period with the following objectives: i) to analyse the use and distribution of zero and that clauses among grammarians; ii) to evaluate the incidence of the phenomenon in correspondence, as an input mirroring the vernacular; and iii) to assess the role of the grammarians’ statements in the propagation of the marked conjunction over the period. [ABSTRACT FROM AUTHOR]

This study explores the concept of morphomic coherence in Spanish PYTA (perfectos y tiempos afines, ‘perfects and related tenses’) verb forms and the notion of the morphome diachronically. The Spanish PYTA form comes from the Latin perfectum, and comprises the preterit, imperfect subjunctives, and future subjunctive. PYTA is marked by a loss of perfective function, and semantic cohesion, causing some to propose that it is morphomic in nature, or, purely morphological with no syntactic or semantic justification. One argument used in support of the notion of the morphome is the concept of “coherence” suggesting that diachronic changes affect all members of the morphome simultaneously. However, analyzing corpus data from the 13-17th century unveils uneven temporal development and inconsistent coherence within speakers. This questions the notion of coherence and provides insights into diachronic shifts in verb morphology. Such corpus results raise questions whether native speakers abstract morphomic patterns over time. [ABSTRACT FROM AUTHOR]

With the increase in infertility cases recorded over the last 20 years, there is a considerable demand for assisted reproductive treatments (ART). However, there is significant variation in the availability of such treatments across different countries. Legislation on assisted reproduction is influenced by various cultural expressions, making it seemingly impossible to create a single representation adaptable to different contexts. This work investigates the cultural representations of ART in Italy and Spain. We collected 1,735 articles from two Italian and Spanish newspapers, with all the articles containing the respective translation of ART from 2013 to 2022. The two corpora were analyzed using the Emotional Text Mining (ETM) methodology. The analysis produced 3 clusters in the Italian corpus and 5 clusters in the Spanish corpus. From the Italian results, a view of ART emerged that is linked to ethical limitations and the ideal of the traditional family. In contrast, the Spanish results depict ART in terms of community, rights, public health, and birth seen in itself. In conclusion, this study highlights the strong differences between Italian and Spanish cultures regarding ART. The results could be used to improve clinical practices and legislation surrounding ART. [ABSTRACT FROM AUTHOR]

While emerging research has contributed significantly to our understanding of the efficacy of parallel corpora in translation education, specifically concerning student performance and perception, however, there remains a noticeable gap in the literature regarding the examination of student engagement with parallel corpora during the translation process. To address this research gap, the present study seeks to comprehensively analyse the behavioural, cognitive, and affective engagement of three MA students when utilizing parallel corpora in Chinese–English translation tasks. A multiple case study design was implemented, drawing upon a diverse range of data sources, including screencasts capturing students' translation processes, the resultant translation outputs, corpus search logs, and in‐depth interviews. The findings of this investigation reveal distinct engagement patterns exhibited by individual students and underscore the intricate interplay of these three dimensions of engagement. Furthermore, student engagement with the parallel corpus significantly influences their translation performance. This research also unveils various factors that impact student engagement patterns, including the perceived affordances of the parallel corpus, students' self‐perception, and learning motivation. [ABSTRACT FROM AUTHOR]

In academic writing, exemplification plays a crucial role in contextualizing complex ideational material through instances the readers can understand. In addition to illustrating ideas through concrete instances, the act of providing examples serves the purpose of helping the readers grasp the writer's intentions. However, strategically performing exemplification to elaborate the propositional material seems to be a challenge for many novice student writers. Although some studies have mentioned that students use significantly less frequently the exemplification resources in their writing, fewer studies have probed into EFL student writing to determine the problems they face in elaborating the ideas. Using the marker approach, which examines the discourse functions bottom‐up from markers to moves, the learner corpus of essay writing (LCEW) was analyzed for three major forms of exemplification: representation, argumentation, and analogy. The results indicate that the examples are strictly limited to certain patterns like specifying concepts through a subcategory and illustrating the arguments through everyday experiences. Moreover, many examples deviate from the usual patterns of exemplification causing confusion. These findings have pedagogic implications for academic writing courses in the EFL context. [ABSTRACT FROM AUTHOR]

The Chinese sequence yǒu rén [exist person] is both the minimal existential-presentational construction and the functional equivalent of indefinite pronouns in encoding indefinite human reference. This dual characteristic prompts the question of whether yǒu rén functions as a presentational construction comparable to similar forms in other languages. Building on a literary corpus of Chinese contemporary novels, this study aims to determine if yǒu rén displays the behavioral properties of presentational constructions. First, presentational yǒu rén constructions are distinguished from both locative-existential and generic-existential ones based on a set of features including predicate selection in the coda, presence and function of the locative expression, and the co-dependent interpretation of the nominal rén. Next, the discourse function of yǒu rén is examined by assessing its contrast with regular nonpresentational sentences and the anaphoric potential of the entity it introduces. While establishing the boundaries between the three construction types, the study also analyzes the co-expression pattern observed within a gradient and compositional approach. [ABSTRACT FROM AUTHOR]

We report the discovery of a vast corpus of indigenous legal contracts ('arraten') in the Morocco Atlas Mountains. The documents comprise about 400 years of written records. A collection of this magnitude has never before been reported, and such documents have not as of yet been systematically amassed for examination, preservation and study. The amount of documents discovered by us, and remaining in situ, is likely to exceed 5000 items. This is in addition to our own private collection which is in excess of 4000 pieces. We estimate that the entire body of documents written on wood in the Anti-Atlas Mountains is likely to be no less than 100,000 pieces and may very well exceed 500,000. In addition to this, between 40,000 and 150,000 paper documents likely exist. Here we report our preliminary studies of those manuscripts. Our collection is available to anyone and donation to suitable institutions will be considered. [ABSTRACT FROM AUTHOR]

Australia has increasingly dependent on China for economic prosperity, but the formidable resources China has brought in its engagement with Australia have been discursively constructed as a threat. Past studies have observed a conventional and often oversimplified binary perception of China–Australia relations (i.e. China opportunity or China threat). Premised on corpus evidences concerning the representation of China in Australian media discourses from 2009 to 2019, this article has shown China threat is considerably more complex than previously observed. China was not only constructed as a security threat but also an economic threat. Australian media contemporary imaginaries of China threat were related to continued fears of "Yellow Peril" and "Red Peril", which spread from the US and reinforced Australia's tendency to dependency. There is a danger of China threat dominating public perceptions of China, drifting into populism of offering simplistic solutions to complex issues. [ABSTRACT FROM AUTHOR]

Short-text clustering, which has attracted much attention with the rapid development of social media in recent decades, is a great challenge due to the feature sparsity, high ambiguity, and massive quantity. Recently, pre-trained language models (PLMs)-based methods have achieved fairly good results on this task. However, two main problems still hang in the air: (1) the significant gap of objective forms in pretraining and fine-tuning, which restricts taking full advantage of knowledge in PLMs. (2) Most existing methods require a post-processing operation for clustering label learning, potentially leading to label estimation errors for different data distributions. To address these problems, in this paper, we propose an Asymmetric Short-Text Clustering via Prompt (short for ASTCP), the features learned with our ASTCP are denser and constricted for clustering. Specifically, a subset text of the corpus is first selected by an asymmetric prompt-tuning network, which aims to obtain predicted label as a clustering center. Then, by the propagation of predicted-label information, a fine-tuned model is designed for representation learning. Thus, a clustering module, such as K-means, is built to directly output clustering labels on top of these representations. Extensive experiments conducted on three datasets have demonstrated that our ASTCP can significantly and consistently outperform other SOTA clustering methods. The source code is available at https://github.com/zhuyi_yzu/ASTCP. [ABSTRACT FROM AUTHOR]

mïmï appears to have diverse applications, appearing in administrative documents, jar labels, depictions of commodity transportation, as well as being used in therapeutic recipes. To date, a consensus for its translation has not been reached. This article re-evaluates the suitability of past identifications in terms of new perspectives in Egyptology, and explores an extended corpus of source material that together suggest a new identification, 'linseed', is more appropriate. The hypothesis is further supported through the recreation and discussion of the materiality of ancient Egyptian medical recipes composed of mïmï and other ingredients with secure translations, and demonstrates: 1) that linseed was a commodity which was taxed, stored, and distributed by the ancient Egyptian administration; 2) that the effect of this product upon the body was observed and harnessed in therapeutic medical recipes. [ABSTRACT FROM AUTHOR]

Copyright of Environmental & Social Management Journal / Revista de Gestão Social e Ambiental is the property of Environmental & Social Management Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This article focuses on metaphorical collocations, a special subtype of lexical collocations in which the cohesion between the constituents is based on a lexicalised metaphor (see Reder 2006; Volungevičienė 2008; Konecny 2010a, 2010b, 2023). The word combinations in question can develop from originally free sequences into recurrent patterns appearing in several semantically related collocations if they occur repeatedly in similar contexts (see Stojić and Košuta 2020, 2021). A case in point is the German collocation die Zeit verfliegt and its equivalents in Croatian, English and Italian: vrijeme leti ('time flies'), time flies (by), and il tempo vola (via) ('time flies (away)'), which convey a rapid movement and the feeling of time rushing by. The various patterns often also reflect cultural concepts and experiences; for example, a pattern that conceptualises time as a resource could be anchored in a cultural idea that regards time as something valuable. Metaphorical collocations thus frequently reflect superordinate conceptual metaphors that are located at a cognitive level and common to more than one language community. Nevertheless, the concrete language-specific manifestations may vary, and in many cases show only partial equivalence. Unlike idioms, where metaphor has long been recognised as a fundamental semantic process (see, e.g., Casadei 1996: passim; Dobrovol'skij and Piirainen 2009: 19–29), collocations have not been thoroughly examined in this regard. It is therefore necessary to gain a more detailed insight into the semantic-cognitive processes underlying the creation of lexical combinations to draw conclusions regarding their relevance for the emergence of collocational sequences. This requires an analysis of extensive authentic language material, which will allow for the development of a catalogue of criteria that facilitates the identification and classification of these specific linguistic phenomena. This paper focuses on uncovering such processes based on a study carried out using SketchEngine in web corpora of German, Croatian, English, and Italian with the aim of creating a clear framework for the identification of metaphorical collocations by elaborating criteria that can serve as guidelines and by attempting a classification into subtypes. The proposed criteria and typology can contribute to the systematic investigation of metaphorical collocations and should also open up perspectives for future work in this field. [ABSTRACT FROM AUTHOR]

Research on named entity recognition (NER) and command-line generation for network security evaluation tools is relatively scarce, and no mature models for recognition or generation have been developed thus far. Therefore, in this study, the aim is to build a specialized corpus for network security evaluation tools by combining knowledge graphs and information entropy for automatic entity annotation. Additionally, a novel NER approach based on the KG-BERT-BiLSTM-CRF model is proposed. Compared to the traditional BERT-BiLSTM model, the KG-BERT-BiLSTM-CRF model demonstrates superior performance when applied to the specialized corpus of network security evaluation tools. The graph attention network (GAT) component effectively extracts relevant sequential content from datasets in the network security evaluation domain. The fusion layer then concatenates the feature sequences from the GAT and BiLSTM layers, enhancing the training process. Upon successful NER execution, in this study, the identified entities are mapped to pre-established command-line data for network security evaluation tools, achieving automatic conversion from textual content to evaluation commands. This process not only improves the efficiency and accuracy of command generation but also provides practical value for the development and optimization of network security evaluation tools. This approach enables the more precise automatic generation of evaluation commands tailored to specific security threats, thereby enhancing the timeliness and effectiveness of cybersecurity defenses. [ABSTRACT FROM AUTHOR]

Copyright of European Journal of Applied Linguistics is the property of De Gruyter and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of European Journal of Applied Linguistics is the property of De Gruyter and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Acknowledgment sections are a rich but underused resource for understanding how language is used for social purposes (such as expressing gratitude and communicating social relations networks), and how conventions and patterns emerge in this process. This paper presents a usage-based case study combining qualitative and quantitative methods for analyzing a dataset of >300 acknowledgment sections from medical dissertations written in German. In our quantitative analysis, we gauge keywords and key n-grams and assess the relative position of recurrent words in each text. Our analysis shows that this text type has developed clear conventions, with acknowledgments in the professional domain being followed by a usually smaller set of expressions of gratitude associated with the private domain. In addition, our quantitative analysis suggests recurrent patterns that can be linked to specific socio-pragmatic functions. For instance, an analysis of n-grams attested in text segments associated with the professional vs. the private domain shows some differences with regard to the typical patterns chosen in those segments. Our analysis also raises a number of future research questions, thus showing that acknowledgment sections are a highly interesting object of study that deserve to be investigated in more detail. [ABSTRACT FROM AUTHOR]

Large language models (LLMs) have recently exhibited significant capabilities in various English NLP tasks. However, their performance in Chinese grammatical error correction (CGEC) remains unexplored. This study evaluates the abilities of state-of-the-art LLMs in correcting learner Chinese errors from a corpus linguistic perspective. The performance of LLMs is assessed using standard evaluation metrics of MaxMatch score. Keyword and key n-gram analyses are conducted to quantitatively explore linguistic features that differentiate LLM outputs from those of human annotators. LLMs' performance in syntactic and semantic dimensions is further qualitatively analyzed based on these probes of keywords and key n-grams. Results show that LLMs achieve a relatively higher performance in test datasets with multiple annotators and low performance in those with a single annotator. LLMs tend to overcorrect wrong sentences, under the explicit prompt of the "minimal edit" strategy, by using more linguistic devices to generate fluent and grammatical sentences. Furthermore, they struggle with under-correction and hallucination in reasoning-dependent situations. These findings highlight the strengths and limitations of LLMs in CGEC, suggesting that future efforts should focus on refining overcorrection tendencies and improving the handling of complex semantic contexts. [ABSTRACT FROM AUTHOR]

The broader aim of this study is the corpus-based investigation of the written language production process. To this end, temporal markers have been keylog recorded alongside the writing processes to exploit pauses to segment the speech product into linear units of performance. However, identifying these pauses requires selecting the relevant interkey intervals (IKI) between production microevents. Different models have been applied to identify such components in the empirical distribution of these delays, in particular Gaussian mixture models (GMM), yet no consensus has emerged regarding the number and interpretation of these components. Here, we analyze IKI distributions from a corpus of keylogs and show that a model with two components is robustly selected across a large range of participants. Furthermore, we show that the contents of these modes are consistent with a fluency vs. disfluency interpretation of the modes by identifying which kinds of events fall into each mode. [ABSTRACT FROM AUTHOR]

Introduction: News media play an important role in shaping public perceptions of and attitudes toward people with autism. This study examines news reports about autism in China Daily over the past 20 years, from 2003 to 2022. Methods: This study employed both quantitative and qualitative analyses. Results: The findings reveal that news coverage on autism predominantly centers on children, with a significant shift in focus from support by family members and schools to support at the broader societal and governmental levels. Further, the overall representation of autism remains predominantly negative and rooted in prejudice. In terms of gender representation, autism is often depicted more frequently as male-oriented. However, a more diverse representation of age groups has emerged. Finally, while news media subtly convey negative attitudes toward autism, they also demonstrate a concurrent concern for the condition, reflecting a complex and nuanced attitude toward people with autism. Discussion: Our study indicates that corpus-assisted critical discourse analysis allows researchers to compare news over time, thus offering insights into changing discourses. The analysis can also reduce bias and uncover the implicit attitudes of news media toward autism by analyzing patterns of words and discourses. [ABSTRACT FROM AUTHOR]

This article presents a pipeline for automated fact-checking leveraging publicly available language models and data. The objective is to assess the accuracy of textual claims using evidence from a ground-truth evidence corpus. The pipeline consists of two main modules—the evidence retrieval and the claim veracity evaluation. Our primary focus is on the ease of deployment in various languages that remain unexplored in the field of automated fact-checking. Unlike most similar pipelines, which work with evidence sentences, our pipeline processes data on a paragraph level, simplifying the overall architecture and data requirements. Given the high cost of annotating language-specific fact-checking training data, our solution builds on the question answering for claim generation method, which we adapt and use to generate the data for all models of the pipeline. Our strategy enables the introduction of new languages through machine translation of only two fixed datasets of moderate size. Subsequently, any number of training samples can be generated based on an evidence corpus in the target language. We provide open access to all data and fine-tuned models for Czech, English, Polish, and Slovak pipelines, as well as to our codebase that may be used to reproduce the results. We comprehensively evaluate the pipelines for all four languages, including human annotations and per-sample difficulty assessment using Pointwise V -information. The presented experiments are based on full Wikipedia snapshots to promote reproducibility. To facilitate implementation and user interaction, we develop the FactSearch application featuring the proposed pipeline and the preliminary feedback on its performance. [ABSTRACT FROM AUTHOR]

The integration of corpus linguistics within translation studies has revolutionised our understanding of mediated language. This study endeavours to advance this burgeoning field by employing a full multi-dimensional analysis to investigate linguistic variation in interpreted language within the specialised context of diplomatic discourse. Specifically, the research examines the co-occurring patterns of linguistic features in interpreted diplomatic language vis-à-vis its non-interpreted counterpart. Employing a multivariate statistical technique, this investigation conducted a factor analysis of 113 linguistic variables, yielding five distinct linguistic dimensions: (1) Involved vs. Informational Production, (2) Objective vs. Addressee-focused Narration, (3) Literate-Oral Continuum, (4) Information Elaboration, and (5) Narrative vs. Non-narrative Concerns. The resulting patterns demonstrate that interpreted diplomatic language tends to be more informative, objective, less elaborated, non-narrative, and aligns more closely with formal registers compared to its non-interpreted counterpart, although both navigate the literate-oral continuum. This study delineates the prevailing co-occurrence patterns in interpreted and non-interpreted diplomatic languages and seeks to elucidate the potential factors shaping these linguistic variations by situating these patterns within the context of diplomatic communication. In doing so, it contributes to a nuanced understanding of how specialised contexts influence mediated language use. The findings have significant implications for corpus-based interpreting studies, shedding light on the multi-dimensional nature of interpreted language and informing the development of targeted pedagogical approaches for diplomatic interpreter training. [ABSTRACT FROM AUTHOR]

Current mainstream for Chinese grammatical error correction methods rely on deep neural network models, which require a large amount of high-quality data for training. However, existing Chinese grammatical error correction corpora have a low annotation quality and high noise levels, leading to a low generalization ability of the models and difficulty in handling complex sentences. To address this issue, this paper proposes a dynamic assessment-based curriculum learning method for Chinese grammatical error correction. The proposed approach focuses on two key components: defining the difficulty of training samples and devising an effective training strategy. In the difficulty assessment phase, we enhance the accuracy of the curriculum sequence by dynamically updating the evaluation model. During the training strategy phase, a multi-stage dynamic progressive approach is employed to select training samples of varying difficulty levels, which helps prevent the model from prematurely converging to local optima and enhances the overall training effectiveness. Experimental results on the MuCGEC and NLPCC 2018 Chinese grammatical error correction datasets show that the proposed curriculum learning method significantly improves the model's error correction performance, with F0.5 scores increasing by 0.9 and 1.05, respectively, validating the method's effectiveness. [ABSTRACT FROM AUTHOR]

Sustainable Development Goals (SDG) 4.7 aims to ensure learners acquire the knowledge and skills for promoting sustainable development by 2030. Yet, Open Educational Resources (OERs) that connect the public with SDGs are currently limitedly assigned and insufficient to promote SDG and sustainability education to support the achievement of SDG 4.7 and other SDGs by 2030, indicating a need for automatic classification of SDG-related OERs. However, most existing labeling systems can not support multiple labeling, tend to generate a large number of false positives, and have poor transferability within the OER domain. This research proposes a method to automatically assign SDGs based on AutoGluon, a machine-learning framework with powerful predictive capabilities, to allow multiple SDGs to be assigned to each OER. In the proposed framework, challenges of category imbalance and limited data availability are addressed, enhancing the precision and applicability of SDG integration in educational resources. To validate the transferability of model knowledge within the OER corpus, we used 900 lecture video descriptions from SDG Academy, forming the foundation for comparing our framework with existing labeling systems. According to the experiment results, our model demonstrates outstanding merits across various metrics, including precision, recall, F1, ACC, AUC, and AP. [ABSTRACT FROM AUTHOR]

Biomedical named entity recognition (BioNER) aims to identify and classify biomedical entities (i.e., diseases, chemicals, and genes) from text into predefined classes. This process serves as an important initial step in extracting biomedical information from textual sources. Considering the structure of the entities it addresses, BioNER tasks are divided into two categories: flat NER, where entities are non-overlapping, and nested NER, which identifies entities embedded within another. While early studies primarily addressed flat NER, recent advances in neural models have enabled more sophisticated approaches to nested NER, gaining increasing relevance in the biomedical field, where entity relationships are often complex and hierarchically structured. This review, thus, focuses on the latest progress in large-scale pre-trained language model-based approaches, which have shown the significantly improved performance of NER. The state-of-the-art flat NER models have achieved average F1-scores of 84% on BC2GM, 89% on NCBI Disease, and 92% on BC4CHEM, while nested NER models have reached 80% on the GENIA dataset, indicating room for enhancement. In addition, we discuss persistent challenges, including inconsistencies of named entities annotated across different corpora and the limited availability of named entities of various entity types, particularly for multi-type or nested NER. To the best of our knowledge, this paper is the first comprehensive review of pre-trained language model-based flat and nested BioNER models, providing a categorical analysis among the methods and related challenges for future research and development in the field. [ABSTRACT FROM AUTHOR]

The conventional approach to event extraction requires predefined event types and their corresponding annotations to train event extractors. However, these prerequisites are often difficult to satisfy in real-world applications. To automatically induct event types, most work has been devoted to clustering event triggers, where a cluster of event triggers is represented as an event type. Some works use trigger semantics, while others use co-occurrence relationships to cluster triggers. However, the clustering results of event triggers obtained by the above work are not sufficiently detailed in describing event types, making it difficult to accurately determine the corresponding event types manually. This paper proposes an open-domain event type induction framework that automatically discovers a set of event types from a given corpus. Unlike previous work on event trigger clustering, this paper takes into consideration the hierarchical relationship of event types to partition the event trigger clusters into event mains and subtypes. The framework employs a latent variable-based neural generation module and a semantic-based clustering module, the former of which obtains event trigger clusters representing the main types of events by jointly projecting the co-occurrence and semantic information of event triggers into a latent space for event type latent variable mining, and the latter of which further divides these event trigger clusters into event subtypes based on semantic information. Finally, experiment results show that, compared with the benchmark model, the ETGen-Clus can improve event type quality scores of 6.23% and 3.11% on the two datasets, respectively. [ABSTRACT FROM AUTHOR]

Basel humanists shaped religious discourse beyond the Reformed sphere through their collections of exempla, short narratives designed to illustrate a moral or doctrinal message. Because scholars of early modern exempla typically focus on either Lutheran or Catholic exempla collections, the role of Reformed compilers as mediators between confessions has been obscured. This article uses methods from the field of digital humanities and corpus linguistics to examine the lasting influence of Theodor Zwinger (1533–1588), Conrad Lycosthenes (1518–1561), and Johannes Herold (1514–1567) on Lutheran and Catholic exempla collections in a newly quantitative way. Using the concordance software AntConc, this study identifies numerous citations that demonstrate that Zwinger and Lycosthenes facilitated confessional cross-pollination of exemplary narratives from the mid-sixteenth through the early eighteenth century. Although citations of Zwinger, Lycosthenes, and Herold are more frequent in Lutheran exempla collections, the existence of several Catholic editions of Zwinger's Theatrum vitae humanae indicates that Catholic readers also valued Zwinger's work. This examination showcases the vital role of Swiss humanists in cross-confessional networks of information exchange and religious discourse in early modern Europe. [ABSTRACT FROM AUTHOR]

Recent advancements in neural machine translation (NMT) offer promising potential for generating cross-language clinical natural language processing (NLP) resources. There is a pressing need to be able to foster the development of clinical NLP tools that extract key clinical entities in a comparable way for a multitude of medical application scenarios that are hindered by lack of multilingual annotated data. This study explores the efficacy of using NMT and annotation projection techniques with expert-in-the-loop validation to develop named entity recognition (NER) systems for an under-resourced target language (Catalan) by leveraging Spanish clinical corpora annotated by domain experts. We employed a state-of-the-art NMT system to translate three clinical case corpora. The translated annotations were then projected onto the target language texts and subsequently validated and corrected by clinical domain experts. The efficacy of the resulting NER systems was evaluated against manually annotated test sets in the target language. Our findings indicate that this approach not only facilitates the generation of high-quality training data for the target language (Catalan) but also demonstrates the potential to extend this methodology to other languages, thereby enhancing multilingual clinical NLP resource development. The generated corpora and components are publicly accessible, potentially providing a valuable resource for further research and application in multilingual clinical settings. [ABSTRACT FROM AUTHOR]

Weighted networks are information-rich and highly-flexible, but they can be difficult to analyze because the interpretation of edges weights is often ambiguous. Specifically, the meaning of a given edge's weight is locally contingent, so that a given weight may be strong for one dyad, but weak for other dyad, even in the same network. I use backbone models to distinguish strong and weak edges in a corpus of 110 weighted networks, and used the results to examine the magnitude of this ambiguity. Although strong edges have larger weights than weak edges on average, a large fraction of edges' weights provide ambiguous information about whether it is strong or weak. Based on these results, I recommend that strong edges should be identified by applying an appropriate backbone model, and that once strong edges have been identified using a backbone model, their original weights should not be directly interpreted or used in subsequent analysis. [ABSTRACT FROM AUTHOR]

In this study a systematic analysis of signaled metaphor is undertaken in naturally occurring discourse from an online forum relating to the experience of depersonalization/derealization, which has a specific relationship with metaphor. While it is relatively easy to locate pre-identified metaphor source terms in such large text corpora, finding singular metaphor that may express subjective experience is recognized as a difficult but important task, which signals of metaphor may support. It is vital to accurately represent, such discourse, rather than only impose pre-defined knowledge, in order that ethical concerns such as exclusion, demographic misrepresentation, and bias confirmation, may be addressed. It is shown that different signals of metaphor investigated perform different functions in relation to metaphor, with many of them supporting use of idiosyncratic metaphor that appears to express the subjective experience of forum participants. Analysis of nouns across all sentences identified as containing signaled metaphor yields previously unknown terms that are typically associated with metaphor in this context, via a method that is grounded in the data. In addition, the summary list of metaphor signaling constructions may support further such investigations in large text corpora. [ABSTRACT FROM AUTHOR]

Objective: In narrow anterior urethral strictures, the combined buccal mucosa graft (BMG) with pedicled penile skin flap (PSF) represents a well‐known effective alternative to staged urethroplasty. We hypothesized that if the native urethral plate and adjacent corpus spongiosum were preserved, a narrower flap would be needed, and reinforced ventral stability could be achieved without compromising the surgical outcome. Methods: Twelve patients with narrow penile urethral strictures underwent single‐stage augmentation urethroplasty using a combined technique. A BMG was quilted to the corpora cavernosa in a dorsal onlay approach, and a longitudinal ventral PSF was transposed ventrally and sutured to the scarred native urethral mucosa on one side and to the BMG on the other side to form a neourethra of triangular form. The preserved corpus spongiosum was wrapped and fixed around the flap ventrally. Results: The median age was 47 years (IQR 35–59), and the median stricture length was 5 cm (IQR 3, 8–7). The median surgical time was 205 min (IQR 172–236). The overall success rate (SR) was 91.7% without sacculation or diverticula formation after a median follow‐up period of 38 months (IQR 33–40). Three transient fistulas healed through prolonged urinary diversion. Five patients (41.7%) reported postvoid dribbling following urethroplasty. Conclusion: Preservation of the native urethral plate is a valuable adjunct to the combination of graft and flap for single‐stage augmentation urethroplasty for narrow urethral strictures, with satisfactory mid‐term success and an acceptable complication rate. [ABSTRACT FROM AUTHOR]

Geoparsers aim to find place names in unstructured texts and locate them geographically. This process produces georeferenced data usable for spatial analyses or visualisations. Much geoparsing research and development has thus far focused on the English language, yet languages are not alike. Geoparsing them may necessitate language-specific processing steps or data for training geoparsing systems. In this article, we applied generic language and GIS resources to geoparsing Finnish texts. We argue that using generic resources can ease the development of geoparsers, and free up resources to other tasks, such as annotating evaluation corpora. A quantitative evaluation on new human-annotated news and tweet corpora indicates robust overall performance. A systematic analysis of the geoparser output reveals errors and their causes at each processing step. Some of the causes are specific to Finnish, and offer insights to geoparsing other morphologically complex languages as well. Our results highlight how the language of the input text affects geoparsing. Additionally, we argue that toponym resolution metrics based on error distance have limitations, and proposed metrics based on spatial intersection with ground-truth polygons. [ABSTRACT FROM AUTHOR]

The study examines a corpus of 306 request emails written by 32 English-speaking (ES) teachers and 121 L2 learners from distinctive L1 backgrounds (i.e., Chinese, French, Spanish) and with different levels of L2 proficiency. Pragmatic competence is analyzed through the coding of direct and indirect request strategies used in formal and informal email writing. Findings reveal the influences of communicative situation, L1 background, and L2 proficiency on pragmatic competence in email writing. First, L2 learners show a significantly lower degree of situational variability compared with ES teachers. Second, L1 backgrounds have a significant impact on L2 writing performance. Third, L2 learners with higher English proficiency tend to use more indirect request strategies, but they have not developed pragmatic competence to adjust their usage across written contexts. Findings are discussed in relation to pedagogical implications for developing writing competence of L2 learners, which should be attuned to diverse rhetorical expectations and individual needs. [ABSTRACT FROM AUTHOR]

Aviation English (AE), a specialized register of English, prioritizes precision, brevity and clarity to maximize aviation safety. While there has been a growing focus within the linguistics community on AE training and assessment since the release of a set of standards and recommended practices, its linguistic properties remain comparatively underexplored. Drawing upon Biber's (1988) multi-dimensional (MD) analysis framework, the present study conducted a corpus-based comparative MD analysis to investigate the multi-dimensional linguistic profile of AE vs. casual conversational English (CE) and exploratory factor analysis (EFA) to extract and interpret the co-occurring linguistic features of routine AE and non-routine AE. The comparative MD analysis shows that AE exhibits more information condensation, less authorial stance and technicality, and fewer features of online information elaboration compared to CE. The EFA shows variations in the linguistic and textual composition of the two sub-registers of AE across two dimensions. Non-routine AE demonstrates a present-focused, viewpoint/intention-oriented approach, involving higher levels of integrative information flow compared to routine AE. Routine AE is characterized by a higher degree of information condensation and is marked by a planned, procedural, and intensive use of standard phraseology. Some pedagogical implications are then proposed for enhancing AE training to cultivate pilots' and air traffic controllers' language competence for precise, unambiguous communication tailored to both routine and non-routine operational contexts. • AE: Dense information, less authorial stance and technicality, limit online elaboration. • Linguistic variations in routine and non-routine AE composition. • Non-routine AE: Present-focused, intention-oriented, integrated information flow. • Routine AE: Information condensed, planned, procedural, SP intensive. • Pedagogical implications for enhancing AE training. [ABSTRACT FROM AUTHOR]

Thesis writing (used here as an umbrella term to cover both master's and doctoral postgraduate-level writing) is a high-stakes genre for postgraduate students. This important student genre has been well-researched from a corpus-based perspective. Corpora of theses and also research articles have been used for data-driven learning (DDL) of this key genre. The purpose of this article is to critically examine key DDL initiatives, some of which take a 'research into practice' orientation. Importantly, the discussion is framed around the notion of 'specificity' in the context of needs analysis, and whether the initiatives take a wide-angle, narrow-angle or move from a wide-angle to a narrow angle approach. Accounts which focus on DIY (do-it-yourself) mini-corpus compilation and use by students are also reviewed. The final section of the article presents a critique of current pedagogic applications, taking a closer look at the issue of 'specificity' within the wider context of needs analysis and mapping out areas for future consideration. It is suggested that an ethnographic perspective may be particularly useful for conceptualising specificity relating to students' present situation needs. The article also considers the impact of AI/ChatGPT on future corpus-based pedagogy of thesis writing. • Disciplinary corpora can usefully be used for teaching thesis writing. • Thesis writing courses using corpora can be viewed along a continuum of a wide-angle and a narrow-angle approach. • Do-it-yourself corpora are an ideal means of achieving 'specificity' in thesis writing courses. • Ethnographic methods can also shed light on the issue of 'specificity' in thesis writing pedagogy. [ABSTRACT FROM AUTHOR]

Group delay processing of audio signals has been extensively studied in the last decade. In this paper, tonic estimation in Turkish makam music is addressed using modified group delay processing. Tonic is one of the stable pitches of the performance, which serves as the reference throughout the performance. The proposed methodology does not require metadata such as the melodic pitch values of the audio files for tonic estimation. Modified group delay functions (MODGD) computed from the flattened music spectrum of non-melodic segments are bin-wise summed to form a summary-modgdgram. The peak position in the tonic range of summary-modgdgram is mapped to the tonic pitch. Two corpora of the Turkish music tonic dataset, created by the CompMusic project group, are used to assess the system. The proposed methodology outperforms the baseline last note detection (LND) method, which uses pitch contour as a prerequisite for tonic estimation. The results show the potential of processing audio signals using the phase-based approach in tonic pitch estimation. [ABSTRACT FROM AUTHOR]

Rapid adaptation to sudden changes in the environment is a hallmark of flexible human behaviour. Many computational, neuroimaging, and even clinical investigations studying this cognitive process have relied on a behavioural paradigm known as the predictive-inference task. However, the psychometric quality of this task has never been examined, leaving unanswered whether it is indeed suited to capture behavioural variation on a within- and between-subject level. Using a large-scale test–retest design (T1: N = 330; T2: N = 219), we assessed the internal (internal consistency) and temporal (test–retest reliability) stability of the task's most used measures. We show that the main measures capturing flexible belief and behavioural adaptation yield good internal consistency and overall satisfying test–retest reliability. However, some more complex markers of flexible behaviour show lower psychometric quality. Our findings have implications for the large corpus of previous studies using this task and provide clear guidance as to which measures should and should not be used in future studies. [ABSTRACT FROM AUTHOR]

Despite the increasing use of digital media data in communication research, a central challenge persists – retrieving data with maximal accuracy and coverage. Our investigation of keyword-based data collection practices in extant communication research reveals a one-step process, whereas our cross-disciplinary literature review suggests an iterative query expansion process guided by human knowledge and computer intelligence. Hence, we introduce the WordPPR method for keyword selection and text data retrieval, which entails four steps: 1) collecting an initial dataset using core/seed keyword(s); 2) constructing a word graph based on the dataset; 3) applying the Personalized PageRank (PPR) algorithm to rank words in proximity to the seed keyword(s) and selecting new keywords that optimize retrieval precision and recall; 4) repeating steps 1–3 to determine if additional data collection is needed. Without requiring corpus-wide sampling/analysis or extensive manual annotation, this method is well suited for data collection from large-scale digital media corpora. Our simulation studies demonstrate its robustness against parameter choice and its improvement upon other methods in suggesting additional keywords. Its application in Twitter data retrieval is also provided. By advancing a more systematic approach to text data retrieval, this study contributes to improving digital media data retrieval practices in communication research and beyond. [ABSTRACT FROM AUTHOR]

Named Entity Recognition (NER) is a crucial task in natural language processing and has a wide range of applications in communication science. However, there is a lack of systematic evaluations of available NER tools in the field. In this study, we evaluate the performance of various multilingual NER tools, including rule-based and transformer-based models. We conducted experiments on corpora containing texts in multiple languages and evaluated the F1-score, speed, and features of each tool. Our results show that transformer-based language models outperform rule-based models and other NER tools in most languages. However, we found that the performance of the transformer-based models varies depending on the language and the corpus. Our study provides insights into the strengths and weaknesses of NER tools and their suitability for specific languages, which can inform the selection of appropriate tools for future studies and applications in communication science. [ABSTRACT FROM AUTHOR]

Topic Modelling has established itself as one of the major text-as-data methodologies within the social sciences in general, and in communications science, in particular. The core strength of TM approaches is the fact that it is essentially a 2-in-1 method, as it both generates clusters into which the texts may fall as well as classifies the texts according to the clusters. Previous research has pointed out that pre-processing text corpora is as much a part of text analysis as the latter stages. Named Entity Recognition, however, is not often thought of when pre-processing texts for analysis and has thus far not received much attention in relation to the TM pipeline. If simply retaining or removing stop words can produce different interpretations of the outcomes of TM, retaining or removing NEs also has consequences on outcomes and interpretations. The current paper analyses the effects that removing/retaining NEs has on the interpretability of topic models. Both model statistics and human validation are used to address this issue. The results show differences in topics models trained on corpora with and without NEs. TMs trained on corpora where NEs are removed exhibit different structural characteristics, and, more importantly, are perceived differently by human coders. We attempt to formulate recommendations regarding the pre-processing of NEs in TM applications. [ABSTRACT FROM AUTHOR]

In this introduction to the special issue celebrating CLLT's 20th anniversary, we look back and forward in time. To look back, we present the results of a (tongue-in-cheek) corpus-linguistic analysis of about 10 years worth of data of research published in CLLT, IJCL, and Corpora in order to distill the "essence" of CLLT for the reader. As an added bonus, we use the opportunity to discuss ways to improve established ways of performing keyness analyses. To look forward, we asked six (teams of) researchers who all have shaped corpus linguistics and thus the journal to give us their take on what the most significant developments in the field have been, and where they see the most impactful opportunities and challenges arise. This introduction briefly summarizes their contributions. [ABSTRACT FROM AUTHOR]