Pattern Language Research Articles

NEW PARADIGM OF CORPUS ANALYSIS OF MILITARY-POLITICAL NEWSPAPERS ARTICLES

This study using the corpus of newspaper articles from The Guardian, The Washington Post, and The New York Times attempts to explore contemporary methods of corpus analysis. It emphasizes that a corpus-driven approach is relevant for improving the technology of balancing text corpora. This approach is characterized by ways of representing corpus data and unifying decoded information based on the corpus. To this end, urgent news and international reports covering the political strategies of various countries in the context of the Russia-Ukraine war were analyzed. It is noted that understanding the essence of the defined issue required outlining the specifics of studying military-political language patterns within the corpus-driven approach. The research found that a variety of data types can influence the formation of the linguistic environment, which explains variations of language system models in textual dimensions. The results also showed random concordance lines, the semantic field of lexemes, and the most frequent units of the journalistic language system that reveal linguistic changes during the war period. Additionally, the study identifies final variables in statistical models, dominantsemantic positions of linguistic constructions, the valency of marked units, and their linguistic potential, status, and functions within the corpus. The importance of using Text Mining technology, the ANNOVA test, and Cortical.io software for data processing is also justified. It is concluded that improving corpus analysis methods depends on text attribution, hierarchical clustering, determining statistically significant differences between mean values of variables through analysis of variance. Moreover, the study enable the conclusion that the text processing mechanism plays an important role in the development of new paradigm of corpus analysis.

Orientational Metaphor in Central Kurdish

This study is entitled "Orientational Metaphor in Central Kurdish". It attempts to explicate how non-spatial concepts are metaphorised in terms of various aspects of space in Central Kurdish. The main aim of the study is to interpret spatial metaphorical expressions that express the cognitive processes of meaning-making. The study adopts the Conceptual Metaphor Theory (CMT)within the framework of cognitive semantics. It deals with orientational metaphor as a specific type of conceptual metaphor. In cognitive semantics, language is a mirror of thought; linguistic expressions are manifestations of conceptual mappings structured in the mind. The data of the study is taken from ordinary everyday Kurdish. The data analysis illuminates the spatialization of a wide variety of abstract concepts through metaphorical uses of space. The study is in line with the cognitive linguists in that linguistic expressions that represent concepts are not semantically independent, as the Anglo-American philosophical tradition claimed, but are structured with respect to one another. Spatial concepts are first structured in thought in the form of a formula based on physical and socio-cultural experiences, and then expressed via linguistic metaphors; accordingly, metaphor is a cognitive phenomenon, it is in thought, not in language. It is patterns of thought that are echoed in patterns of language.

Mobility Justice: An Ecolinguistic Perspective

The climate crisis, migration and urbanization may appear as three separate crises, but under Sheller’s paradigm of Mobility Justice, they become part of a coherent whole that should be tackled as a single, complex and interconnected predicament. This paper observes rhetorical strategies employed in texts about the climate crisis, about cycling advocacy and about the “climate lockdown” conspiracy theory, which developed in Oxford, UK, in 2023. The metaphors, deictic pronouns and identity categories used are the main discourse features analysed through a qualitative approach, showing how mobility-related issues are often discussed through spatial metaphors, while deictic pronouns play a central role in the creation of identities. The findings are employed to contribute to the beneficial reframing of mobility-related discourses, whether global or local, and to react to climate inaction. The overall aim of this approach is to reveal the links between discourses about the climate crisis on a global scale and those on a local, urban scale concerning urban mobility policies. The prism through which both global and local discourses are observed is that of space and access to mobility. The aim of this investigation is to identify new patterns of language that can help us finding “new stories to live by”.

ANALISIS NILAI-NILAI AGAMA DAN MORAL DALAM TAYANGAN UPIN IPIN SEASON 5 IKHLAS DARI HATI PADA ANAK KELOMPOK A DI RA MAMBAIL FALAH

The aim of this study is to explore and elucidate the religious and ethical principles conveyed in the Upin Ipin season 5 episode titled "Ikhlas dari Hati," and to assess its influence on children's spiritual and moral growth. This research employs a descriptive qualitative approach, utilizing both primary and secondary data sources. Data gathering methods encompass observation, interviews, and documentation, while data analysis involves content analysis, data scrutiny, and reconstruction. The findings reveal that "Ikhlas dari Hati" episode of Upin Ipin portrays various religious and moral values, including integrity, acceptance, kindness, charity, assistance, collaboration, discipline, and reverence. Interview outcomes further demonstrate that Upin Ipin episodes can positively impact children's character development, fostering behaviors such as treating others with kindness and respect, offering assistance, and instilling other constructive values. Furthermore, this influence extends beyond mere knowledge acquisition, also influencing children's language patterns in their daily interactions. This occurs as children tend to readily emulate what they observe and hear, swiftly integrating these behaviors into their everyday lives. Keywords : Religious and Moral Values, Early Childhood, Upin Ipin Films

Study of Language Function in Bengali-Speaking Population with Motor Neuron Disease.

Motor neuron diseases (MNDs) have been traditionally considered to spare cognition. But recent findings show that multiple domains of cognition including language can be involved in MND patients. Most studies on language patterns of MND patients were conducted in Western nations, but data on Indian population is limited. This study is an attempt to explore the language functions of Bengali-speaking MND patients from this part of eastern India. To determine the prevalence and nature of language dysfunction in MND patients. A single-center, hospital-based, observational, cross-sectional study. The Bengali adaptation of the Western Aphasia Battery was administered to 50 cases diagnosed with MND, attending a tertiary care hospital consecutively over a 1-year period, and fulfilling the inclusion and exclusion criteria for the study. Descriptive and inferential statistics were used for expressing results. Eighteen percent of cases showed impairments in spontaneous speech. Fluency was impaired in 72%, and 22% cases showed impaired naming. Moreover, 20% and 26% of cases were impaired in repetition and comprehension, respectively. Reading and writing was impaired in 16% and 26% of cases, respectively. Significant difference was found in the primary language skill scores and aphasia quotient across age groups, while no significant difference was found in these scores across education status. This study describes the language profiles of Bengali-speaking MND patients from eastern India, and the findings are similar to previous research works, which have shown morpho-syntactic, lexical-semantic, and phonological errors in language function.

Exposition aux événements traumatogènes et coping : analyse lexicométrique du discours de patients en service de victimologie

AimExposure to traumatic events is a common phenomenon in the general population. Indeed, according to international epidemiological studies, 80% of individuals have been exposed to at least one traumatic event during their lifetime. Traumatic events can lead to post-traumatic (post-traumatic stress disorder (PTSD) specific symptoms) and dissociative symptoms (PTSD nonspecific symptoms). However, few French studies in the field of the epidemiology and psychopathology of traumatic events take into account the emotional experience in individuals exposed to a traumatic event. Our research focus on (1) the characteristics of traumatic events and (2) the strategies to cope with it. MethodFourteen patients met at a victimization service participated in this study. The sample includes three men and eleven women aged between 21 and 54 (mean age=37.07 years-old; standard deviation=10.10). For the recruitment of patient, each patient was approached by the psychiatrist, head of the unit, to participate in the study during a consultation. An individual appointment was scheduled with patients for conducting the interview. Interviews were transcribed and we produced a lexicometric analysis with the software Iramuteq, based on the Alceste method. The Alceste method is a technique that consist in a modeling of the distribution of words in a speech and language patterns to identify the most words used by the subjects. ResultsResults demonstrated, (a) four types of traumatic events to which the patients were exposed (car accidents, interpersonal violence, domestic and intra-family violence), but the intensity of the subjective experience of the event does not appear in a specific class, and (b) that among the strategies used to cope with it, the use of active and problem-centered coping strategies appears explicitly in two specific classes: seeking for social support (18.40% of the corpus) and searching for information and understanding (19.10% of the corpus). DiscussionWe categorized the traumatic events to which the patients were personally exposed, nevertheless, as an event has a different impact on victims, the intensity of the subjective experience of the traumatic event was not clearly highlighted in the patient's speech. This finding suggests the need to focus on the potentially traumatic nature of the event in order to better understand the psychopathological repercussions. We described the use of adapted strategies (i.e., social support, the search for information and solutions) that may mediate the negative effects of the experience of traumatic events on the physical and mental health of victims. The use of emotional-related strategies may increase the conditional risk of developing post-traumatic stress disorder.

An SFL Analysis of Dialogues in Elementary EFL Textbooks

Textbooks play a vital role in language instruction. The study aims to examine the three metafunctions of language based on the Systemic Functional Linguistics (SFL) theory. As such, a descriptive qualitative approach along with content analysis is employed to examine dialogues that were selected conveniently from two elementary EFL textbooks. The findings indicate that the relational process is the most frequent process type indicating the ideational function of the two textbooks. For the sake of interpersonal function, declarative sentences are mostly utilized. Meanwhile, the textual function is mostly reflected by the T (Thing) and DT (Deictic + Thing) nominal group pattern. These findings have pedagogical implications for teaching English as a Foreign Language (EFL), particularly for elementary school students; providing simple language patterns that help students build a foundational vocabulary and understanding of grammar for proficient communication. The theoretical implication of the findings is the importance of scaffolded learning that helps learners grasp basic linguistic concepts before moving on to more complex and dynamic language use

Reversal of the Word Sense Disambiguation Task Using a Deep Learning Model

Word sense disambiguation (WSD) remains a persistent challenge in the natural language processing (NLP) community. While various NLP packages exist, the Lesk algorithm in the NLTK library demonstrates suboptimal accuracy. In this research article, we propose an innovative methodology and an open-source framework that effectively addresses the challenges of WSD by optimizing memory usage without compromising accuracy. Our system seamlessly integrates WSD into NLP tasks, offering functionality similar to that provided by the NLTK library. However, we go beyond the existing approaches by introducing a novel idea related to WSD. Specifically, we leverage deep neural networks and consider the language patterns learned by these models as the new gold standard. This approach suggests modifying existing semantic dictionaries, such as WordNet, to align with these patterns. Empirical validation through a series of experiments confirmed the effectiveness of our proposed method, achieving state-of-the-art performance across multiple WSD datasets. Notably, our system does not require the installation of additional software beyond the well-known Python libraries. The classification model is saved in a readily usable text format, and the entire framework (model and data) is publicly available on GitHub for the NLP research community.

Impact of Social Media on English Language Learning Among First-Year Students at Qassim University

The diverse aspects and networks of social media have wielded a remarkably profound impact on both individual lives and the broader societal, economic, and political landscape of its users. Language patterns used by conversational partners on social media have been influenced significantly. This study aims to investigate how the English language learning process of non-native Saudis has been impacted by the use of social media. To investigate these effects, a quantitative methodology was employed through the distribution of an online questionnaire to selected groups of first-year students at Qassim University, Saudi Arabia. Responses from 47 participants from different genders were analyzed using measures of central tendency and other statistical measures. The findings revealed that first-year Qassim University students consider social media platforms to be beneficial for improving their L2 proficiency and evaluate them to have an overall positive impact on various language learning skills. Based on these findings, it is reasonable to indicate that social media can be a significantly helpful tool in improving English language learning. These results were subjective to variables related to gender and the multimodal nature of specific aspects of social media. The study implies that employing social media networks can illustrate a more student-centered approach to language learning through the facilitation of interactive collaboration and information exchange, resulting in enhanced syntactic and lexical complexity.

Language Choices and Digital Identity of High School Student Text Messages in the New Capital City of Indonesia: Implication for Language Education

This research investigates the language choices and digital identities represented in text messages exchanged by high school students in the New Capital City of Indonesia (Ibu Kota Nusantara/IKN) and the implications of these linguistic practices for language teaching. Using a corpus of messages transmitted via WhatsApp collected from 100 high school students, this study produced 2.1 million tokens of 83,414 word types. This study uses corpus linguistic analysis to investigate the distinctive features of language usage, lexical changes, and communication trends in the digital discourse of IKN high school students. Data analysis utilizes features from AntConc corpus tools, including word lists, collocations, concordances, and N-grams. Concerning the features uncovered the subtle nuances that contribute to the unique linguistic identity of the student authors. This research highlights how digital language patterns attribute individual identity within the context of IKN high school students. The data analysis discovers three linguistic patterns in the electronic texts produced by IKN High School students, reflecting their general digital identity as language users in IKN, namely (1) lexical choice, (2) orthographic selection, and (3) lexical bundles. Furthermore, the study reveals the complex construction of digital identity through language, with students negotiating social positions, connections, and personal identities via their linguistic choices in electronic texts. The research findings contribute to the implications of language teaching in the IKN area and a deeper understanding of how students within the IKN area express themselves through language in the virtual realm, thus shaping their digital identities.

The Pragmatics of Gaze Patterns in a Local Family Sign Language from Guatemala

In this study, we document the coordination of eye gaze and manual signing in a local sign language from Nebaj, Guatemala. We analyze gaze patterns in two conversations in which signers described the book Frog Where Are You to an interlocutor. The signers include a deaf child who narrated the book to a hearing interlocutor and her grandfather, who is also deaf, as he described the same book to his hearing grandson during a separate conversation. We code the two narratives for gaze target and sign type, analyzing the relationship between eye gaze and sign type as well as describing patterns in the sequencing of eye gaze targets. Both signers show a strong correlation between sign type and the direction of their eye gaze. As in previous literature, signers looked to a specialized medial space while producing signs that enact the action of characters in discourse in contrast to eye gaze patterns for non-enacting signs. Our analysis highlights both pragmatic–interactional and discursive–narrative functions of gaze. The pragmatic–interactional use of gaze primarily relates to the management of visual attention and turn-taking, while the discursive–narrative use of gaze marks the distinction between narrator and character perspective within stretches of narration.

Echoes of Culture: Relationships of Implicit and Explicit Attitudes With Contemporary English, Historical English, and 53 Non-English Languages

Attitudes are intertwined with culture and language. But to what extent? Emerging perspectives in attitude research suggest that cultural representations in language are more related to implicitly measured (vs. explicitly measured) attitudes, and that such relationships persist across history and diverse languages. We offer a comprehensive test of these ideas by correlating (a) attitudes toward 55 topics (e.g., Rich/Poor, Dogs/Cats, Love/Money) from ~100,000 U.S. English-speaking participants with (b) representations of those same topics in word embeddings from contemporary English text, 200 years of English books, and 53 non-English languages. Strong and robust relationships emerged between representations in contemporary English and implicitly but not explicitly measured attitudes. Moreover, strong correlations with implicitly measured attitudes persisted across 200 years of books, and most non-English languages. Results provide new insights into the nature of implicitly measured attitudes and how they are intertwined with cultural representations that are relatively hidden in patterns of language across time and place.

Revealing the Unseen: AI Chain on LLMs for Predicting Implicit Data Flows to Generate Data Flow Graphs in Dynamically-Typed Code

Data flow graphs (DFGs) capture definitions (defs) and uses across program blocks, which is a fundamental program representation for program analysis, testing and maintenance. However, dynamically-typed programming languages like Python present implicit data flow issues that make it challenging to determine def-use flow information at compile time. Static analysis methods like Soot and WALA are inadequate for handling these issues, and manually enumerating comprehensive heuristic rules is impractical. Large pre-trained language models (LLMs) offer a potential solution, as they have powerful language understanding and pattern matching abilities, allowing them to predict implicit data flow by analyzing code context and relationships between variables, functions, and statements in code. We propose leveraging LLMs’ in-context learning ability to learn implicit rules and patterns from code representation and contextual information to solve implicit data flow problems. To further enhance the accuracy of LLMs, we design a five-step Chain of Thought (CoT) and break it down into an AI chain, with each step corresponding to a separate AI unit to generate accurate DFGs for Python code. Our approach’s performance is thoroughly assessed, demonstrating the effectiveness of each AI unit in the AI Chain. Compared to static analysis, our method achieves 82% higher def coverage and 58% higher use coverage in DFG generation on implicit data flow. We also prove the indispensability of each unit in the AI Chain. Overall, our approach offers a promising direction for building software engineering tools by utilizing foundation models, eliminating significant engineering and maintenance effort, but focusing on identifying problems for AI to solve.

Everyday positivity: An appraisal analysis of online identity in food blogs

This paper analyses a number of “About me” sections of Danish food blogs to the purpose of investigating how bloggers discursively construct online identities. The blogs belong to a hybrid genre, containing both commercial products (recipes) and the personal narratives of bloggers on their everyday lives. Because of the highly subjective character of the narratives, the appraisal framework from systemic functional linguistics was chosen as an analytical tool to uncover patterns in evaluative language. Through the selection of content and the way it is discursively presented, bloggers seek to create a bond with an imagined audience, with whom they form a so-called ambient affiliation. Two such bonds are identified: a positive-thinking bond and a feeling-of-safety bond.

Development of the Fetal Brain Corticocortical Structural Network during the Second-to-Third Trimester Based on Diffusion MRI.

During the second-to-third trimester, the neuronal pathways of the fetal brain experience rapid development, resulting in the complex architecture of the interwired network at birth. While diffusion MRI-based tractography has been employed to study the prenatal development of structural connectivity network (SCN) in preterm neonatal and postmortem fetal brains, the in utero development of SCN in the normal fetal brain remains largely unknown. In this study, we utilized in utero dMRI data from human fetuses of both sexes between 26 and 38 gestational weeks to investigate the developmental trajectories of the fetal brain SCN, focusing on intrahemispheric connections. Our analysis revealed significant increases in global efficiency, mean local efficiency, and clustering coefficient, along with significant decrease in shortest path length, while small-worldness persisted during the studied period, revealing balanced network integration and segregation. Widespread short-ranged connectivity strengthened significantly. The nodal strength developed in a posterior-to-anterior and medial-to-lateral order, reflecting a spatiotemporal gradient in cortical network connectivity development. Moreover, we observed distinct lateralization patterns in the fetal brain SCN. Globally, there was a leftward lateralization in network efficiency, clustering coefficient, and small-worldness. The regional lateralization patterns in most language, motor, and visual-related areas were consistent with prior knowledge, except for Wernicke's area, indicating lateralized brain wiring is an innate property of the human brain starting from the fetal period. Our findings provided a comprehensive view of the development of the fetal brain SCN and its lateralization, as a normative template that may be used to characterize atypical development.

Exploring the influence of user characteristics on verbal aggression towards social chatbots

ABSTRACT Chatbots possess great potential benefits, yet concerns persist regarding users adopting inappropriate, offensive language. This research delved into the influence of user characteristics on verbally aggressive behaviours towards social chatbots. Employing a mixed-method study, we examined individual characteristics such as personal dispositions, offensive language patterns, academic majors, and prior experiences with conversational agents. Findings from a ten-day field experiment involving 33 participants using a real-world Telegram-based chatbot app unveiled that users' anthropomorphism, computer-related major, and gender significantly impact their moral emotions and evaluations of the chatbot's capabilities. Moreover, employing offensive language towards the chatbot detrimentally impacted users' perceptions of its abilities, helpfulness, and likability. The research findings advocate for ongoing monitoring and effective resolution of users' behaviours regarding the use of offensive language in their interactions with a chatbot. Additionally, the results underscore the importance of incorporating diverse perspectives into chatbot design to address biases and offensive utterances.

All Watched Over by Our Data Double

Abstract The term data double denotes information generated by and collected from users of networked communications to construct relational databases in marketing and other domains. Each subject (user) of this surveillance-panoptical system inevitably informs and objectifies all other subjects. Social media experiences are based on our continuously “tracked” engagement. Surveillance functions across written and spoken language, biometrics, geolocation, and visual and behavioral patterns. This text is primarily concerned with visual media and its production and (re)circulation as the accumulation of data through uploading, viewing, liking, commenting, remixing, and sharing. The article explores how selected media artists reflect upon the potential of recirculating information to reveal our data doubles and the surveillance-panoptical system.

Humour, politainment and fandom: A semiotic-narrative analysis of unofficial profiles of Spanish politicians

In today’s media landscape, concepts like Americanization and professionalization of politics are evolving, driven by technological advancements and social media usage. The internet has transformed political communication, emphasizing emotional appeal and user response, leading to a rise in politainment and celebrity politicians. This shift towards ‘pop politics’ blurs the line between entertainment and information, with virality and humour playing key roles. This study conducts a semiotic-narrative analysis of unofficial social media profiles of Spanish political representatives, examining their language, content, and usage patterns resembling fan communities. Parodic Twitter profiles of prominent figures like Pedro Sánchez, Yolanda Díaz and Isabel Díaz Ayuso, along with Ada Colau's Instagram account, are analyzed. The findings reveal a trend towards employing politainment strategies to engage social media users, fostering emotional connections akin to non-political fan communities.

The Image of the Patriot and the Enemy of the People in Selected Documents from the State Security Archive

The paper explores the linguistic devices and strategies used by State Security collab-orators to characterize the personalities and actions of targets of operational interest. On the one hand, the text examines documents from the State Security archives (re-cruitment offers and recruitment reports) that describe the ways in which new collabo-rators were recruited. On the other, the descriptions of the subjects of the reports in the reports are of interest. The research focuses on the communicative language pat-terns used and described in the relevant documents. As a source of excerpted material for scientific analysis, we used the book "Pastors Agents of the State Security Service" by Angel Pilev. Through an applied combination of research methods - lexical-semantic and discourse analysis, we search for lexical means with connotative load, as well as characterizing lexemes, building the opposite images - of the patriot informer and the enemy of the people reasons. By applying the close reading method, we aim at explicating the reader's point of view with minimal historical context. The linguistic material shows that the documents of the State Security build a very negative image of the enemy of the people. The strategy of the recruiters is also analyzed, emphasizing patriotic feelings, patriotic duty, expression of love for the motherland, and the noble deed to be done by the agents. The text emphasizes that it is to linguistics, along with other humanities that expectations are addressed for an objective, publicly accessible, and convincing interpretation of the era of socialism.

Development of a Sign Language recognizer using LSTM

Sign language recognition plays a crucial role in bridging communication gaps between the deaf community and the hearing population. This paper presents the development of a sign language recognizer using Long Short-Term Memory (LSTM) networks, a type of recurrent neural network well-suited for sequence prediction tasks. The proposed model aims to accurately interpret hand gestures into text, enhancing accessibility and communication for individuals with hearing impairments. Our approach utilizes a custom-built dataset of sign language gestures, which undergoes preprocessing to extract relevant features such as hand position, orientation, and movement. The LSTM-based architecture is designed to capture the temporal dynamics of these gestures, enabling the recognition of complex sign language patterns. We train the model using a supervised learning approach. Our method demonstrates superior accuracy in recognizing sign language gestures compared to traditional machine learning methods and other deep learning architectures. This advancement contributes significantly to the field of assistive technologies. Future work will explore the integration of additional modalities, such as facial expressions and body movements, to further enhance the system's performance and usability.