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Using the Central Andean Chavín Phenomenon as a case study, this paper explores the middle ground between top-down (big data) and bottom-up (Bayesian modeling) approaches to archaeological radiocarbon data. Compiling radiocarbon dates associated with the Chavín Phenomenon addresses questions of chronology, demonstrating that the relevant phases at interacting sites are relatively brief and broadly contemporary. In addition, the definition of a discrete span of time associated with the Chavín Phenomenon makes it possible to explore the context for that period of heightened interaction. Juxtaposing that timespan with a compilation of Central Andean radiocarbon dates identifies contemporary non-participatory sites, enabling characterization of the sociopolitical milieu within which the Chavín Phenomenon emerged, flourished, and faded. The identification of that corpus of sites also highlights the importance of a shift in focus from identification of interaction to characterization of interaction. Bottom-up approaches to radiocarbon chronology will be a key element of that effort, while high-level summary of radiocarbon assemblages can identify where additional dating and Bayesian modeling can have the greatest interpretive impact. [ABSTRACT FROM AUTHOR]

A phrase-frame (p-frame) is a multi-word sequence with a one-word variable within the sequence (e.g., it is * to). P-frames are important components of language production and can demonstrate phraseological patterning. This study examined p-frames retrieved from one learner business emails corpus (1,413 texts based on the Education First-Cambridge Open Language Database) and one working professional email corpus (1,145 texts from the Enron email dataset). P-frames were investigated both quantitatively and qualitatively in terms of their structural characteristics, functional characteristics, and variability. Our results showed that the working professionals and the learners of business English used p-frames differently. The working professionals used p-frames in ways that aligned with written conventions, whereas the learners of business English used p-frames in ways that did not accord well with written conventions. This difference was detected by comparing tendencies in function-word frames and frames for referential function. In addition, p-frames used by the working professionals displayed a higher degree of variability than those by the learners of business English. This study facilitates an understanding of learners' p-frame use in English for business purposes and suggests that p-frames be incorporated into the teaching and learning of L2 business writing. [ABSTRACT FROM AUTHOR]

Analyzing the diachrony of Hebrew bein, originally a spatial preposition 'within/among', this article traces several grammaticization clines associated with bein's gradual evolution into a rich variety of functions within various constructions. While its early function is purely locative, bein later participates in an array of constructions and sub-constructions, where its syntactic status, its semantic meanings and functions, as well as its discourse role, have shifted considerably. Bein may thus be part of a conjunctive or a disjunctive construction, but it may also be part of an 'in any case' adverbial, and later an 'anyway' discourse marker. It conveys different locative construals ('within', 'among' and 'between'), but also various abstract symmetric relations, such as 'difference', 'dispute' and 'separation'. Some bein constructions partition a set, focusing on a subset of it, while in other cases bein serves as an 'all-purpose' preposition. Nevertheless, our goal is to show how each grammatical, semantic and discoursal phase is well motivated, given the previous phase. Based on written historical corpora, from Biblical to contemporary Hebrew, we provide a step-by-step diachronic development from earlier to later bein functions. [ABSTRACT FROM AUTHOR]

The aim of this article is to offer a systematic review of digital studies that provide new research perspectives on ancient classical theatre. The undeniable progress in the field of computational analysis in the service of traditional textual interpretation is helping to study in greater depth and to interpret in greater detail the classical linguistic corpora that have come down to us through the manuscript tradition. The new model of digital research is integrated not only in the field of information technologies, but also in the field of e-learning, where we can already observe the implementation of a new educational model. Based on the digital processing of data on Greco-Roman theatre, a systematic review is presented, following the methodological principles of the PRISMA statement [ Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2009 (2020)]. [ABSTRACT FROM AUTHOR]

This research focuses on classifying translated and non-translated Chinese texts by analyzing syntactic rule features, using an integrated approach of machine learning and entropy analysis. The methodology employs information entropy to gauge the complexity of syntactic rules in both text types. The methodology is based on the concept of information entropy, which serves as a quantitative measure for the complexity inherent in syntactic rules as manifested from tree-based annotations. The goal of the study is to explore whether translated Chinese texts demonstrate syntactic characteristics that are significantly different from those of non-translated texts, thereby permitting a reliable classification between the two. To do this, the research calculates information entropy values for syntactic rules in two comparable corpora, one of translated and the other of non-translated Chinese texts. Then, various machine learning models are applied to these entropy metrics to identify any significant differences between the two groups. The results show significant differences in the syntactic structures. Translated texts have a higher degree of entropy, indicating more complex syntactic constructs compared to non-translated texts. These findings contribute to our understanding of the effect of translation on language syntax, with implications for text classification and translation studies. [ABSTRACT FROM AUTHOR]

This article will analyze the use of the noun nafs ‘soul’ with the firstperson possessive pronominal suffix, through the corpus of Andalusi texts gathered in Nafḥ al-ṭīb by the North-African author Shihāb al-Dīn al-Maqqarī (d. 1632). The aim is threefold: one, to identify patterns of the use of nafsī in the Nafḥ, their semantic performance, and diachronic evolution; two, to compare the use of the term in this corpus with its use in lists of collocates in the macro corpora KSUCCA and arTenTen of Classical and Modern Standard Arabic; and three, to show that linguistically systematizing self-expression is adequate for the identification of highly subjective texts in a corpus. Analysis will show that the notions of the ‘divided self’, sacrificing oneself, and yearning change towards a closer relationship between the subject and the self over time. [ABSTRACT FROM AUTHOR]

Despite the critical role that datasets play in how systems make predictions and interpret the world, the dynamics of their construction are not well understood. Drawing on a corpus of interviews with dataset creators, we uncover the messy and contingent realities of dataset preparation. We identify four key challenges in constructing datasets, including balancing the benefits and costs of increasing dataset scale, limited access to resources, a reliance on shortcuts for compiling datasets and evaluating their quality, and ambivalence regarding accountability for a dataset. These themes illustrate the ways in which datasets are not objective or neutral but reflect the personal judgments and trade-offs of their creators within wider institutional dynamics, working within social, technical, and organizational constraints. We underscore the importance of examining the processes of dataset creation to strengthen an understanding of responsible practices for dataset development and care. [ABSTRACT FROM AUTHOR]

The phenomenon of the 'complaint tradition', expressing the belief that language standards are deteriorating in some way, has had a long and vibrant existence in discussions of English in Hong Kong. The contribution of a Cultural Linguistics perspective here highlights the nature and significance of the cultural conceptualisations of language, for example, those that are revealed by the use of metaphor. The article begins with an overview of the discourse in Hong Kong, based on a corpus of media articles. The discourse structure and content of the articles, including their metaphorical aspects, are then analysed in more detail. To help understand the persistence of the discourse I draw on the insight that metaphors constitute 'mini‐narratives' which construct identities and identity boundaries. The discussion considers how the prevailing conceptualisations of English—some general, and others more culturally specific—serve to reproduce dominant, standardising views and inhibit the acceptance of local variation. [ABSTRACT FROM AUTHOR]

Based on the analysis of a linguistic corpus, this study uses relevance theory (Sperber and Wilson, 1986/1995) to demonstrate that the Tunisian Arabic particle ha :, which has never received any research attention, acts as an attitudinal marker in addition to its vocative function. Namely, ha: is restricted to utterances in which the speaker is expressing negative emotion or a subtype of positive emotion that can only be directed at the addressee. The data analysis shows that, unlike its relatively unmarked and well-studied counterpart ya: , ha: is restricted in terms of its frequency of distribution and the types of speaker emotions and attitudes expressed in the utterance in which it occurs. This study argues that ha: encodes a procedure which instructs the hearer to recognize the speaker's emotion or attitude expressed toward them in the utterance. The particle ha: therefore, serves as an explicit additional guarantee of relevance that encourages the hearer to look for additional contextual assumptions in order to achieve the speaker's desired interpretation. • The choice between the two vocative particles ha: and ya: is not arbitrary. • The Tunisian Arabic particle ha: acts as an attitudinal marker. • ha: encodes a procedure guiding the hearer to recognize the speaker's emotions. • The particle ha: serves as an explicit additional guarantee of relevance. • The particle ha : is no always compatible with the speaker's preferences. [ABSTRACT FROM AUTHOR]

Speech enhancement performance has improved significantly with the introduction of deep learning models, especially methods based on the Long–Short-Term Memory architecture. However, these methods face challenges such as high computational complexity and redundancy of input features. To address these issues, we propose a U-Net-based approach that utilizes an encoder/decoder to extract more concise features, thereby enhancing single-channel speech performance and reducing computation complexity. The proposed method includes a Cross-Swin-Transformer block and a masked bottleneck module, which down-samples features while preserving the detailed representation through skip connections and carefully designed blocks. The bottleneck module extracts coarse representations of hidden features as masks. We evaluated our method against other U-Net-based approaches on VCTK and DNS corpora using CBAK, eSTOI, PESQ, STOI, and SI-SDR metrics. The results demonstrate that the proposed method achieves promising performance while significantly reducing computational complexity. [ABSTRACT FROM AUTHOR]

The learning quality of humans depends on the sense of contemplation. Textual documents are a huge part of the literature on contemplation which effortlessly creates perception. Automatic document class identification or organisation is a machine learning function to understand the psychological and emotional content of the text in a concise way. The problem of identification of documents falls in the field of library science, information science and artificial intelligence. The research progress of class identification of documents has been made in various most spoken languages. Numerous research works have been published in European and Asian languages. However, there is a gap in the literature when it comes to any less resource language, especially Bengali. Consequently, this work portrays an efficient topic modelling approach for Bengali document class identification. It proposes a Dirichlet-polynomial clustering model likelihood corpus distribution (LCD), which is based on a Bayesian numerical prototype. Experiments are done to prove the efficiency of LCD over various topic modelling algorithms, such as latent Dirichlet allocation (LDA), LDA with bag-of-words (LDA-BOW), latent semantic indexing (LSI), and hierarchical Dirichlet process (HDP). For performance evaluation, we considered five real-world datasets of Bengali corpora, such as science, sports, computer, season, and epic in this work. The coherence score of different modelling algorithms is compared to find the best model for each dataset separately. [ABSTRACT FROM AUTHOR]

The ability to assign meaning to perceptual stimuli forms the basis of human behavior and the ability to use language. The meanings of things have primarily been probed using behavioral production norms and corpus-derived statistical methods. However, it is not known to what extent the collection method and the language being probed influence the resulting semantic feature vectors. In this study, we compare behavioral with corpus-based norms, across Finnish and English, using an all-to-all approach. To complete the set of norms required for this study, we present a new set of Finnish behavioral production norms, containing both abstract and concrete concepts. We found that all the norms provide largely similar information about the relationships of concrete objects and allow item-level mapping across norms sets. This validates the use of the corpus-derived norms which are easier to obtain than behavioral norms, which are labor-intensive to collect, for studies that do not depend on subtle differences in meaning between close semantic neighbors. [ABSTRACT FROM AUTHOR]

Grounding language in vision is an active field of research seeking to construct cognitively plausible word and sentence representations by incorporating perceptual knowledge from vision into text-based representations. Despite many attempts at language grounding, achieving an optimal equilibrium between textual representations of the language and our embodied experiences remains an open field. Some common concerns are the following. Is visual grounding advantageous for abstract words, or is its effectiveness restricted to concrete words? What is the optimal way of bridging the gap between text and vision? To what extent is perceptual knowledge from images advantageous for acquiring high-quality embeddings? Leveraging the current advances in machine learning and natural language processing, the present study addresses these questions by proposing a simple yet very effective computational grounding model for pre-trained word embeddings. Our model effectively balances the interplay between language and vision by aligning textual embeddings with visual information while simultaneously preserving the distributional statistics that characterize word usage in text corpora. By applying a learned alignment, we are able to indirectly ground unseen words including abstract words. A series of evaluations on a range of behavioral datasets shows that visual grounding is beneficial not only for concrete words but also for abstract words, lending support to the indirect theory of abstract concepts. Moreover, our approach offers advantages for contextualized embeddings, such as those generated by BERT (Devlin et al, 2018), but only when trained on corpora of modest, cognitively plausible sizes. Code and grounded embeddings for English are available at (https://github.com/Hazel1994/Visually_Grounded_Word_Embeddings_2). [ABSTRACT FROM AUTHOR]

The semantic understanding of numbers requires association with context. However, powerful neural networks overfit spurious correlations between context and numbers in training corpus can lead to the occurrence of contextual bias, which may affect the network’s accurate estimation of number magnitude when making inferences in real-world data. To investigate the resilience of current methodologies against contextual bias, we introduce a novel out-of-distribution (OOD) numerical question-answering (QA) dataset that features specific correlations between context and numbers in the training data, which are not present in the OOD test data. We evaluate the robustness of different numerical encoding and decoding methods when confronted with contextual bias on this dataset. Our findings indicate that encoding methods incorporating more detailed digit information exhibit greater resilience against contextual bias. Inspired by this finding, we propose a digit-aware position embedding strategy, and the experimental results demonstrate that this strategy is highly effective in improving the robustness of neural networks against contextual bias. [ABSTRACT FROM AUTHOR]

Objective: This study proposes a cephalometric method to determine an individual radius of the curve of Spee in cases of occlusal prosthetic rehabilitation. Methods: A retrospective study was conducted on 469 lateral cephalograms. Statistical analysis was performed. Correlations were computed using simple regression, Z tests, paired t-tests, and one-way repeated measures ANOVA. Results: Regression calculations supported a link between the radius of the curve of Spee and occlusal plane inclination, Balkwill's angle, mandibular parameters (MP), with MP = corpus – ramus + Bonwill height. Based on MP, data that are accessible even in the absence of teeth, a regression formula can be proposed to provide an individualized estimation of the radius of the Spee curve (RCS): RCS = 0.624 * MP + 26.583. Conclusion: This study proposes an individualized estimation of the RCS from only skeletal mandibular parameters: the length of the ramus, length of the corpus, and the Bonwill height. [ABSTRACT FROM AUTHOR]

Copyright of Procesamiento del Lenguaje Natural is the property of Sociedad Espanola para el Procesamiento del Lenguaje Natural 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 Procesamiento del Lenguaje Natural is the property of Sociedad Espanola para el Procesamiento del Lenguaje Natural 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 Procesamiento del Lenguaje Natural is the property of Sociedad Espanola para el Procesamiento del Lenguaje Natural 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 Procesamiento del Lenguaje Natural is the property of Sociedad Espanola para el Procesamiento del Lenguaje Natural 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 Procesamiento del Lenguaje Natural is the property of Sociedad Espanola para el Procesamiento del Lenguaje Natural 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.)

Latent Dirichlet allocation (LDA) is a popular method for analyzing large text corpora, but it suffers from instability due to its reliance on random initialization. This results in different outcomes for replicated runs, hindering reproducibility. To address this, we introduce LDAPrototype, a new approach for selecting the most representative LDA run from multiple replications on the same dataset. LDAPrototype enhances the reliability of LDA conclusions by ensuring greater similarity between replications compared to traditional LDA runs or models chosen based on perplexity or NPMI. A key feature of LDAPrototype is its use of a novel model similarity measure called S-CLOP (Similarity of multiple sets by Clustering with LOcal Pruning). It is based on topic similarities, for which we compare the usage of measures like the thresholded Jaccard coefficient, cosine similarity, Jensen-Shannon divergence, and rank-biased overlap. The effectiveness of LDAPrototype is demonstrated through its application to six real datasets, including newspaper articles and tweets. The results show improved reproducibility and reliability in topic modeling outcomes. LDAPrototype's approach is noteworthy for its practical applicability, comprehensibility, ease of implementation, and computational efficiency. Furthermore, the algorithm's concept can be generalized to other topic modeling procedures that characterize topics through word distributions, making it a versatile tool in text data analysis. [ABSTRACT FROM AUTHOR]

Determining the sentiment of a written text is an important task in text research. This task can be performed either in the supervised or unsupervised version. In this paper, we propose a novel unsupervised algorithm for documents written in any language using documents written in Polish as an example. The clustering of Polish language texts with respect to their sentiment is poorly developed in the literature on the subject. The novelty of the proposed algorithm involves the abandonment of stoplists and lemmatisation. Instead, we propose translating all documents into English and performing a two-stage document grouping. In the first step of the algorithm, selected documents are assigned to a class of positive or negative documents based on a set of lexical and grammatical rules as well as a set of keyterms. Key-terms do not have to be entered by the user, the algorithm finds them. In the second step, the remaining documents are attached to one of the classes according to the rules based on the vocabulary found in the documents grouped in the first step. The algorithm was tested on three corpora of documents and achieved very good results. [ABSTRACT FROM AUTHOR]

The present study is a contrastive interlanguage analysis of propositional idea density in the written language of native English speakers and Asian learners of English. The study adopted a quantitative method to analyse the ICNALE written essays v2.4 using CPIDR v5.1 software. The study aimed to identify the level of propositional idea density in the writing of English native speakers and Asian learners of English and to examine the effect of the type of English, the theme type, the proficiency level and nationality on propositional idea density. The study is one of the very few studies about propositional idea density in ESL and EFL contexts. Many statistical analyses were performed on CPIDR results using jamovi v2.3 software, and the results of the tests revealed that the propositional idea density in native speakers' essays was higher than that in Asian learners' essays. Moreover, the results showed that there was a statistically significant difference among all the participants based on the theme types of part-time jobs and smoking, among native speakers based on occupation type (students, teachers, others) and among Asian learners of English based on the type of English proficiency level and nationality. The findings of the study showed that propositional idea density is a psycholinguistic index of the amount of presupposed knowledge language users have on a specific topic. In other words, propositional idea density is a window to language users' schemata. Additionally, the findings of the study can be considered an insightful contribution to contrastive interlanguage analysis. [ABSTRACT FROM AUTHOR]

The present research deals with the phenomenon of hedging language, mainly focusing on the context of spontaneous verbal interactions. The main aim of the study is to analyze the occurrence of hedges in English spoken discourse as captured in the Spoken British National Corpus 2014. The research process deals with the differences in the usage of approximators and shields existing among men and women across different age groups and levels of education. The research methodology involves creating six sub-corpora, measuring hedge dispersion, examining selected hedges to ascertain their hedging function within the context and calculating frequencies within each sub-corpus. The research outcomes point to the prevalence of shields in spoken discourse. Plausibility shields are commonly used while attribution shields show lower usage. Adaptors contribute to mitigating certainty in utterances, whereas rounders play a less prominent role. These findings emphasize the nuanced interplay between linguistic strategies and speaker characteristics in spoken communication and highlight the importance of cooperative principles in discourse. [ABSTRACT FROM AUTHOR]

This paper introduces a method, for creating research-driven ontology to foster collaboration and innovation. The concept of collaborative innovation implies a process where multiple stakeholders work together to generate novel ideas, solutions or products. The suggested approach combines Artificial Intelligence (AI) and expert knowledge to build a comprehensive model encompassing various aspects of research, development and innovation. To demonstrate the feasibility of this method, the paper showcases its implementation in the field of accounting science. First, AI-powered machine-learning algorithms and text-mining techniques are used to extract the main ontological elements from a large corpus of accounting literature. Subsequently, expert knowledge is utilized to refine and validate these identified elements. The resulting ontology can be used as the foundation of a knowledge-based system to promote collaboration and analyze the state of innovation. [ABSTRACT FROM AUTHOR]

Stylized neural machine translation (NMT) aims to translate sentences of one style into sentences of another style, it is essential for the application of machine translation in a real-world scenario. Most existing methods employ an encoder-decoder structure to understand, translate, and transform style simultaneously, which increases the learning difficulty of the model and leads to poor generalization ability. To address these issues, we propose a curriculum pre-training framework to improve stylized NMT. Specifically, we design four pre-training tasks of increasing difficulty to assist the model to extract more features essential for stylized translation. Then, we further propose a stylized-token aligned data augmentation method to expand the scale of pre-training corpus for alleviating the data-scarcity problem. Experiments show that our method achieves competitive results on MTFC and Modern-Classical translation dataset. [ABSTRACT FROM AUTHOR]

Few-shot relation classification (RC) is a crucial task that aims to identify the relationships between entity pairs with limited mentions. However, this task is challenging due to the insufficient amount of annotated data. Augmenting the quantity of annotated instances provides a reasonable solution, which is typically realized through distant supervision (DS). However, this approach may introduce substantial noise and negatively impact model performance. Conversely, prompt learning (PL) improves the quality of annotated instances by enabling the models to understand the input text thoroughly with templates. Unfortunately, these templates mainly neglect the semantics of labels. To address these issues, we propose coarse-to-fine filters with concept heuristic prompt (CoFF-CHP), which employs dual-stage filters to select and label reliable instances from unlabeled corpus. Specifically, we first employ the coarse-grained filters to preliminarily select the instances. Next, we design a fine-grained filter, concept heuristic prompt (CHP), which can integrate the semantics of the label concept to help the model understand the link between the input text and labels. Finally, to reduce the tilt toward positive instances, we incorporate the screen-out data and false-positive instances to form the negative set to correct the classifier. The experimental results on the public Fewrel dataset indicate that CoFF-CHP achieves state-of-the-art performance in low-resource scenarios, especially when the best baseline is upgraded by 15.06% in terms of F1 in the setting of 10 seed instances. [ABSTRACT FROM AUTHOR]

The launch of the FrameNet project in 1997 was both a crystallisation point of decades worth of theoretical investigations into lexical meaning by Charles J. Fillmore and colleagues, as well as the seed of an ongoing line of corpus-based and computational research that seeks to implement Fillmore's theory of Frame Semantics in a way that both provides an interesting model relevant for further theorising and also is applicable practically for semantic analysis, lexicology, and lexicography. At the occasion of FrameNet's 25th birthday, we want to introduce the project to a new generation of researchers but also take stock of, and report on, what has been achieved. We revisit the origins of the FrameNet project, assess its development and various changes in the years since it was first reported on in a special issue in the pages of this journal twenty years ago. [ABSTRACT FROM AUTHOR]

The current study examines the interpretations of eight English-Arabic labels for food products found in the Saudi market. The study of translations is examined with regard to the translation techniques utilized under Venuti's concepts of domestication and foreignization. Specifically, the study focuses on analyzing the common translation techniques employed for food product labels and aims to assess if these methods align with Venuti's favored approach of foreignization. Furthermore, it investigates whether these techniques have led to the original message being altered or not. This study conducts a quantitative analysis that compares various aspects. Calculations are conducted on the frequencies and percentages of techniques identified to obtain accurate statistical results. The findings reveal that foreignization is emphasized by the extensive use of literal translation, with transliteration, borrowing, and non-translation being comparatively less frequent in the translation of the studies' food product labels. Techniques under the domestication strategy include transposition as the most frequently used, followed by adaptation, and addition and omission are both used equally, with the least frequently used techniques falling under domestication. The study also determines that the effectiveness of techniques categorized as foreignization and domestication varies in the corpus analyzed. [ABSTRACT FROM AUTHOR]

Using the Attitude resources framework from Appraisal Theory, this study qualitatively and quantitatively analyzes the distribution characteristics of attitude resources in the climate change news discourse of China Daily. By employing Python programs to collect data and establish a small-scale corpus, the study reveals the discourse strategies adopted by China's external propaganda media in constructing a national image. The findings suggest that, in terms of affect, China has successfully constructed its image as a responsible major power; in terms of judgement, China is endowed with moral traits of fairness, rationality, and cooperation for mutual benefit; and aesthetically, China is portrayed as a leader in global climate governance. [ABSTRACT FROM AUTHOR]

The current research focuses on dialogic pragmatics in social valves, which are disclosed through various pragmatic techniques. It primarily seeks to clarify the variations in dialogic pragmatism in some selected Quranic verses from Surah Al-Isra that contain a social valve and how Allah Almighty commands us to worship not only him but also our parents. The study's corpus evaluates the applicability of dialectical relevance by containing many speech events, stories, and conversations and explaining the significance of appraising the speaker intending from the speaker rather than the hearer, as well as the double role of the (speaker-hearer). To summarise, this work proposes that pragmatic theories reflect dialogic pragmatics and relevance dialogic, which should be considered vital components of dialogic research for demonstrating various social valves. [ABSTRACT FROM AUTHOR]

Sentiment analysis refers to analyzing people's emotional tendencies expressed in subjective texts. Emotional dictionary and machine learning-based analysis methods are the main research methods. Appraisal theory, developed by linguist J. R. Martin based on systematic linguistics, is mainly divided into three subsystems: engagement, attitude, and graduation. Based on the attitude system of appraisal theory, this paper analyzes 158 overseas purchase evaluations of the Xiaomi mobile phone using the emotion dictionary. 158 corpora were evaluated based on the selection of adjectives, adverbs, and negative words as categories of the Emotional Dictionary from 500 online shopping reviews and their ratings as criteria for emotional attitudes. The results prove that consumers generally have a positive attitude towards Xiaomi mobile phones, which are embodied in battery performance, mobile phone fluency, and camera, while the network compatibility and overall performance of mobile phones show a relatively negative emotional tendency. Therefore, this research can help consumers and businesses conduct sentiment analysis in a more comprehensive and efficient way, promote purchase decisions, and enhance product competitiveness. This study also provides enlightenment for the combination of appraisal theory and sentiment analysis. [ABSTRACT FROM AUTHOR]

In this paper, I show how pragmatics and syntax are interconnected in Old English by examining the left-dislocation system in Wulfstan's homilies. Syntactically, this article argues that left-dislocation fits in nicely with the system found in other Old English texts, despite certain superficial structural differences. The unexpected high number of demonstrative resumptives is accounted for by the accumulation of formulaic structures in one homily. Pragmatically, LFD performs a number of discourse functions. The main function is a generalizing one, as LFD introduces new topics in the discourse. This case study also shows that other functions traditionally linked with LFD can be found in Wulfstan's texts. For instance, demonstrative resumptives show some degree of topic shifting and can be accompanied by the contrastive function. By contrast, personal pronoun resumptives can mark topic continuity with specific referents. Since the corpus data are necessarily limited because only one file from the YCOE is examined, some claims are not verifiable. A good example is the assumption found in the literature that personal pronoun resumptives in LFD tend to land low in the clause. [ABSTRACT FROM AUTHOR]

John Lovejoy coined the term biological diversity in 1980, made the first projection of global extinction rates, and 43 years later we are still discussing biodiversity and extinction in an inconsequential fashion. Extinction signs include the loss of millions of birds in the UK since 1970 and the decline in insects. Goulson summarizes in detail the scientific and biological evidence for the many species extinctions. Although most people do not notice the declines in insects, the loss of bees has been noted when bees' use as pollinators began to be harnessed as a corporate and commercial activity. This is linked to intensive agricultural practices. The lobbying power of agrochemical companies shapes agricultural practices that directly impact the well-being of all species. Critical ecological discourse analysis of insect decline and the issues related to it is employed, going back to the famous speech given by Michael Halliday. Then corpus linguistic methods scrutinize material from the website of Syngenta, an agrochemical company. We ask whether the website of such a firm can uncover the necessary circumstances for such biodiversity. A corpus-assisted critical analysis of Syngenta's business report, looks at computer-generated concordances of some of the relevant content words, like 'crop', 'sustainable', 'soil', 'control', 'biodiversity' and 'water'. Hopefully, this study will encourage researchers to provide more indications of the disappearance of so many species, and not just birds and insects. But, to really achieve effective protection of biodiversity much more is needed. [ABSTRACT FROM AUTHOR]

Research article abstracts, the second most-read part of research papers after titles, generally follow disciplinary conventions, which are often manifested in their language use. This study analyzed lexical bundles or multi-word sequences in move texts of a one-million-word corpus of English-language medical research article abstracts, with particular attention to vocabulary levels. The most frequent lexical bundles, such as "the primary end point was", often occurred once per text and predominantly took part in realizing a move. The coverage of the first thousand New General Service List was 63.6% for the entire corpus but was around 80% for bundles in Move 3, describing principal results, and those in Move 4, evaluating the results. Many of the sequences were research-oriented bundles, used to express research contexts. The bundles were made up of relatively accessible word items, but the sequences occurred to realize highly specific research contexts. The findings suggest that becoming familiar with the bundle may need increasing awareness of disciplinary conventions such as guideline adherences and statistical procedures. This study may offer insights on the need for learners to familiarize themselves with these bundles. [ABSTRACT FROM AUTHOR]

Copyright of O Social em Questão is the property of Faculdades Catolicas - Pontificia Universidade Catolica do Rio de Janeiro 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.)

Gender inequalities are linguistically constructed and indexed through the strategic use of language representing the power structure in the physical world. Recently, #MeTooIndia, an Indian version of the global feminist movement, provided a scholarly opportunity to explore the gender stereotypes of emotional expressions. Even though some researchers have studied the sociocultural dimensions of #MeToo, the corpus-based linguistic analysis of this movement has yet to be explored significantly. Therefore, the current study investigated the discursive association of emotional representation of masculine and feminine genders in media texts. Given the societal nature of Indian patriarchal ideologies, power and gender stereotypes, we hypothesize that Indian mass media, as a powerful political actor, may attribute more positive lexicons to the masculine pronoun he and more negative markers to the feminine pronoun she. Textual genres of #MeToo were extracted from the print media by using the corpus framework of collocational and concordance methods. Our results revealed that, though the news of #MeToo heightened the feminine actor and largely produced the pronoun she in the referential position, the narratives of texts assigned more positive emotive markers to the masculine social actors. Overall, this study concludes that #MeToo as a gender movement has faced strategic linguistic resistance against feminine sexual victims, favouring the masculine actors. [ABSTRACT FROM AUTHOR]

Hierarchical Text Classification (HTC) aims to match text to hierarchical labels. Existing methods overlook two critical issues: first, some texts cannot be fully matched to leaf node labels and need to be classified to the correct parent node instead of treating leaf nodes as the final classification target. Second, error propagation occurs when a misclassification at a parent node propagates down the hierarchy, ultimately leading to inaccurate predictions at the leaf nodes. To address these limitations, we propose an uncertainty-guided HTC depth-aware model called DepthMatch. Specifically, we design an early stopping strategy with uncertainty to identify incomplete matching between text and labels, classifying them into the corresponding parent node labels. This approach allows us to dynamically determine the classification depth by leveraging evidence to quantify and accumulate uncertainty. Experimental results show that the proposed DepthMatch outperforms recent strong baselines on four commonly used public datasets: WOS (Web of Science), RCV1-V2 (Reuters Corpus Volume I), AAPD (Arxiv Academic Paper Dataset), and BGC. Notably, on the BGC dataset, it improves Micro-F1 and Macro-F1 scores by at least 1.09% and 1.74%, respectively. [ABSTRACT FROM AUTHOR]

Extracting valuable information from biomedical texts is one of the current research hotspots of concern to a wide range of scholars. The biomedical corpus contains numerous complex long sentences and overlapping relational triples, making most generalized domain joint modeling methods difficult to apply effectively in this field. For a complex semantic environment in biomedical texts, in this paper, we propose a novel perspective to perform joint entity and relation extraction; existing studies divide the relation triples into several steps or modules. However, the three elements in the relation triples are interdependent and inseparable, so we regard joint extraction as a tripartite classification problem. At the same time, from the perspective of triple classification, we design a multi-granularity 2D convolution to refine the word pair table and better utilize the dependencies between biomedical word pairs. Finally, we use a biaffine predictor to assist in predicting the labels of word pairs for relation extraction. Our model (MCTPL) Multi-granularity Convolutional Tokens Pairs of Labeling better utilizes the elements of triples and improves the ability to extract overlapping triples compared to previous approaches. Finally, we evaluated our model on two publicly accessible datasets. The experimental results show that our model's ability to extract relation triples on the CPI dataset improves the F1 score by 2.34% compared to the current optimal model. On the DDI dataset, the F1 value improves the F1 value by 1.68% compared to the current optimal model. Our model achieved state-of-the-art performance compared to other baseline models in biomedical text entity relation extraction. [ABSTRACT FROM AUTHOR]

The article discusses the integration of corpora into language teaching, emphasizing its benefits and the challenges in mainstream adoption. Topics include the underuse of corpora in teaching due to a lack of user-friendly resources, the potential for teachers to enhance their instructional decisions and materials through corpus-based insights, and the author's observations of students learning to use a corpus in an academic writing module.

This paper analyses the methodologies in 148 empirical data‐driven learning studies for L2 English in prestige journals to examine best practice. Manual coding and corpus analysis of key words and n‐grams from the past 5 years (2018–2022) explore the field as a whole and how methodologies have evolved, suggesting improvements and future avenues for research. [ABSTRACT FROM AUTHOR]

This paper introduces readers to the TESOL Quarterly special topic issue: Assessing the effectiveness of corpus‐based approaches to English language teaching. The authors open by briefly defining and outlining data‐driven learning (DDL) research on second language (English) teaching. This is followed by an extended discussion of four themes, which pertain to recent developments and future directions in DDL. These themes include: (1) selecting corpora for corpus‐based language teaching; (2) pushing the boundaries of DDL; (3) preparing language educators for corpus‐based language teaching; and (4) reflecting on theory and methods. Throughout the piece, readers are also introduced to those contributions within the special issue, which include a diverse range of pieces that focus on aspects of theory, research, and pedagogy. [ABSTRACT FROM AUTHOR]

The use of academic corpora in second language (L2) writing pedagogy has gained popularity in recent decades, particularly in genre‐specific contexts for graduate‐level L2 students (Charles, 2007; Lee & Swales, 2006). However, its overall effectiveness is mainly observed within classroom contexts, influenced by various contextual and participant‐related factors (Vyatkina & Boulton, 2017). Moreover, these investigations have predominantly relied on retrospective data useful for understanding learners' perception of corpus‐analysis experiences, but not for how learners strategically integrate corpora into personal toolkits for self‐directed writing tasks. Addressing these gaps, this study tracks five Chinese EFL graduate students' corpus use through a 5‐week concept‐ and corpus‐based tutoring intervention and investigates how they navigated a discipline‐specific corpus alongside other digital resources during post‐intervention self‐directed revisions. By triangulating screen recordings with retrospective data (questionnaires, interviews, draft revisions, and stimulated recalls), the results showed that while participants were generally favorable toward the specialized corpus and its complementary role to personal toolkits, individual problem‐solving patterns in revision revealed less overall reliance on the corpus and its effectiveness primarily in addressing known linguistic problems. Implications for a relational understanding of digital tools support and the integration of other pedagogical activities in L2 writing instruction are discussed. [ABSTRACT FROM AUTHOR]

Data‐driven learning (DDL) is an increasingly popular area of research for language teaching and is gradually being incorporated into teacher education programs globally. However, assessing teacher trainees' (self‐reported) corpus literacy, and its impact on eventually (and successfully) incorporating corpora into language teaching, is an ongoing issue. This paper explores the experiences of in‐service English language teacher trainees in developing both corpus literacy and corpus‐based language pedagogy skills in Vietnam. The study finds a disconnect between participants' high self‐reported corpus literacy skills, high intention to conduct corpus‐based language teaching, and their ability to convert said intention and literacy into viable ideas for corpus‐based language teaching within their lesson planning. Potential reasons include difficulties conceptualizing DDL's integration into lesson objectives, selection of inappropriate corpora for the target audience, issues planning DDL activities for primary‐age learners, and Vietnamese‐specific issues mainly around access to technology. Accordingly, we present several recommendations for improving the assessment of DDL trainees' actual integration of DDL into their teaching practice. [ABSTRACT FROM AUTHOR]

Learner corpora have been used extensively in corpus research to identify gaps and errors within learner writing but have rarely been directly used in corpus‐informed instruction (CII). Importantly, scholars in CII have pointed out that "it is as important to see what learners can do as well as what they can't" when using learner corpora (Boulton & Thomas, 2012; p. 13). Learner corpora can also offer greater alignment with the genres students are writing (Seidlhofer, 2002). In our Brief Report, we take up Lu, Casal, and Liu's (2021) call for greater synergy of genre and corpus pedagogy by examining the impact of using a learner corpus to enhance students' language awareness and genre‐specific knowledge within an English as an Additional Language (EAL) first‐year writing classroom. We also move beyond error analysis to encourage an asset‐based model to learner CII (Staples, 2022). [ABSTRACT FROM AUTHOR]

This paper discusses the development and implementation of a locally‐sourced corpus to address the specific needs of an ESL writing program. The paper begins with a description of the motivation and development of the Corpus of Ohio Learner and Teacher English (COLTE), a large in‐house corpus consisting of assessed ESL student writing and teacher feedback from first‐year writing courses. The paper illustrates research conducted within the local context using the COLTE and discusses how the program's corpus‐based approach influenced curricular revisions and instructional practice. It addresses the evaluation of these efforts, challenges encountered, and considerations for building and using corpora at the program level. The paper concludes by arguing that stakeholders within programs can work collaboratively to build and use locally‐sourced corpora grounded in their specific setting to explore local practices and address the English language learning and teaching needs of their own particular context. [ABSTRACT FROM AUTHOR]

Although there has been a plethora of research on formulaic language in contemporary English, conducted with various purposes in mind (descriptive, applied and otherwise), studies of formulaic phrasings in Old English texts are rare. In this paper, we employ selected corpus linguistic methods to identify and explore the use and discoursal functions of recurrent multi-word items that contribute the most to the formulaicity of homilies, chronicles and biblical translations, which are the Old English text types under scrutiny. The findings of this primarily descriptive and exploratory research provide new insights into the pragmatic functions of Old English recurrent phraseological units as well as into the structure and communicative functions of the analysed text varieties. Finally, the results of the study cast some new light on the role those formulaic phrasings play in Old English prose. [ABSTRACT FROM AUTHOR]

In recent years the amount of written vernacular Arabic has increased dramatically. But encoding an unstandardized language in writing is not straightforward and mechanical; rather, it is a complex process that balances practical considerations with ideological stances such as autonomy from the standard language. This study examines how writers of Tunisian Arabic (or derja) are navigating this elaboration process. Using a quantitative analysis of a 279,000-word corpus of print literary works written in Tunisian Arabic (2014–2021) and a 5.8-million-word corpus of internet forum posts (2010–2021), this paper explores how Tunisians writing in derja make orthographic choices to collectively position themselves in relation to the larger Arab world. The study finds that forum writers who have advocated for an improved status for Tunisian Arabic use more phonemic spelling forms—more closely representing Tunisian pronunciation—while those who have advocated for Standard Arabic are more conservative in their spelling choices. The authors of Tunisian Arabic novels and translations—pulled between issues of elaboration and readability—are a bit more conservative than the pro-derja group. These results show how writers in unstandardized or not-yet-standardized languages like Tunisian Arabic use orthography to express national identity stances in relation to supranational languages and identities. [ABSTRACT FROM AUTHOR]

Copyright of Pravnik is the property of Czech Academy of Sciences, Institute of State & Law 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 International Journal of Language Academy is the property of Rota Kariyer 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.)