Hasil untuk "Language. Linguistic theory. Comparative grammar"

Shalom Lappin

Noam Chomsky

Eric Antwi

In contemporary Ghanaian senior high schools, WhatsApp has emerged as a central medium for leadership communication, merging institutional authority with the informal dynamics of online interactions. This shift creates new avenues where power is linguistically constructed, negotiated, and contested. The study explores how senior high school leaders utilise language in WhatsApp messages to assert and maintain authority over teachers. While prior research on educational leadership has predominantly focused on structural or behavioural dimensions of power, there has been limited attention paid to how authority is linguistically enacted in mobile-mediated communication. Guided by French and Raven’s (1959) five-base typology of power, the study employed a qualitative single case study design to investigate how linguistic choices reflect different bases of authority. The data consisted of sixty-eight (68) screenshot messages produced by twelve (12) school administrators, one (1) Head of School, three (3) Deputy Heads, and eight (8) Heads of Department. Purposive sampling was employed to select messages that illustrated leadership power, and the data were analysed thematically. The study revealed that three of the five power bases coercive, legitimate, and reward were evident in the leadership discourse. Two distinct subtypes of coercive power situational and discursive emerged, offering new insights into how authority is linguistically articulated in institutional online contexts. The study concludes that language serves as a vital resource for legitimising authority in digital school leadership. These findings suggest that leadership training should focus on the strategic and balanced use of language to promote collaboration, motivation, and a positive organisational culture.

Mingucci, Giulia

This paper explores the role of perceptual reiteration in the formation of experience according to Aristotle’s psychology and epistemology. Beginning with an analysis of sense perception (aisthesis), it examines the retention of perceptual traces (phantasia) and their reiteration in experience (empeiria). This ultimately establishes the fundamental role of sense perception in grounding our epistemic and practical relationship to reality, revealing a novel perspective on how reiteration shapes the behaviour of both humans and non-humans.

Katarzyna Maniowska

Quanto pesa una virgola nel testo? Quanto vale l’aggiunta o l’omissione della virgola nella traduzione? Un diverso uso della punteggiatura può comportare differenze semantiche? La lettura parallela del testo della Costituzione della Repubblica Italiana e della sua traduzione polacca ci offrirà il punto di partenza per ulteriori riflessioni sul significato di questo elemento tanto labile. L’analisi comparativa verrà preceduta da brevi cenni sullo sviluppo dell’interpunzione in italiano e in polacco, nonché da osservazioni sugli usi diversi della punteggiatura in entrambe le lingue. Dal momento che l’interpunzione italiana è di tipo prosodico-sintattica, mentre il polacco opta per l’interpunzione basata su rigide regole sintattiche, quest’incongruenza dei sistemi di interpunzione è rilevante anche dal punto di vista traduttivo. Infatti, una corretta trasposizione del senso viene effettuata anche attraverso la punteggiatura. La Costituzione della Repubblica Italiana appartiene alla categoria dei testi vincolanti, perciò richiede dal traduttore la massima rigidità interpretativa. Si ipotizza che, nonostante diversi criteri di applicazione della virgola in entrambe le lingue, sia il testo originale che il testo tradotto esprimano lo stesso significato senza maggiori deviazioni di senso. Attraverso esempi tratti dal prototesto si individueranno possibili punti di incontro nel sistema di interpunzione italiano e polacco, nondimeno saranno evidenziate le differenze inconciliabili che derivano dalla diversa concezione della punteggiatura in ambedue le lingue.

Ajitesh Bankula, Praney Bankula

Cross-lingual transfer has become a crucial aspect of multilingual NLP, as it allows for models trained on resource-rich languages to be applied to low-resource languages more effectively. Recently massively multilingual pre-trained language models (e.g., mBERT, XLM-R) demonstrate strong zero-shot transfer capabilities[14] [13]. This paper investigates cross-linguistic transfer through the lens of language families and morphology. Investigating how language family proximity and morphological similarity affect performance across NLP tasks. We further discuss our results and how it relates to findings from recent literature. Overall, we compare multilingual model performance and review how linguistic distance metrics correlate with transfer outcomes. We also look into emerging approaches that integrate typological and morphological information into model pre-training to improve transfer to diverse languages[18] [19].

Antara Raaghavi Bhattacharya, Isabel Papadimitriou, Kathryn Davidson et al.

Across languages, numeral systems vary widely in how they construct and combine numbers. While humans consistently learn to navigate this diversity, large language models (LLMs) struggle with linguistic-mathematical puzzles involving cross-linguistic numeral systems, which humans can learn to solve successfully. We investigate why this task is difficult for LLMs through a series of experiments that untangle the linguistic and mathematical aspects of numbers in language. Our experiments establish that models cannot consistently solve such problems unless the mathematical operations in the problems are explicitly marked using known symbols ($+$, $\times$, etc., as in "twenty + three"). In further ablation studies, we probe how individual parameters of numeral construction and combination affect performance. While humans use their linguistic understanding of numbers to make inferences about the implicit compositional structure of numerals, LLMs seem to lack this notion of implicit numeral structure. We conclude that the ability to flexibly infer compositional rules from implicit patterns in human-scale data remains an open challenge for current reasoning models.

Brendon Boldt, David Mortensen

We introduce CSAR, an algorithm for inducing morphemes from emergent language corpora of parallel utterances and meanings. It is a greedy algorithm that (1) weights morphemes based on mutual information between forms and meanings, (2) selects the highest-weighted pair, (3) removes it from the corpus, and (4) repeats the process to induce further morphemes (i.e., Count, Select, Ablate, Repeat). The effectiveness of CSAR is first validated on procedurally generated datasets and compared against baselines for related tasks. Second, we validate CSAR's performance on human language data to show that the algorithm makes reasonable predictions in adjacent domains. Finally, we analyze a handful of emergent languages, quantifying linguistic characteristics like degree of synonymy and polysemy.

Aruna Sankaranarayanan, Dylan Hadfield-Menell, Aaron Mueller

All natural languages are structured hierarchically. In humans, this structural restriction is neurologically coded: when two grammars are presented with identical vocabularies, brain areas responsible for language processing are only sensitive to hierarchical grammars. Using large language models (LLMs), we investigate whether such functionally distinct hierarchical processing regions can arise solely from exposure to large-scale language distributions. We generate inputs using English, Italian, Japanese, or nonce words, varying the underlying grammars to conform to either hierarchical or linear/positional rules. Using these grammars, we first observe that language models show distinct behaviors on hierarchical versus linearly structured inputs. Then, we find that the components responsible for processing hierarchical grammars are distinct from those that process linear grammars; we causally verify this in ablation experiments. Finally, we observe that hierarchy-selective components are also active on nonce grammars; this suggests that hierarchy sensitivity is not tied to meaning, nor in-distribution inputs.

Naiba Jamalzade

This article examines syntactic structures in contemporary German and their theoretical foundations. The study analyzes sentence structure, syntactic units, and construction types within the framework of modern linguistic theories, including generative grammar, functional syntax, and construction grammar. Special attention is paid to the interaction between form and meaning, as well as to the role of syntax in discourse and communication. The article demonstrates that syntactic constructions in modern German are dynamic and context dependent, reflecting both grammatical rules and pragmatic factors. The findings contribute to a deeper understanding of German syntax and may be useful for linguistic research, language teaching, and comparative studies.

Solafa Tareq Abdullah

Cultural Words in English do not always perform the same function in Arabic because of the cultural differences between the two languages. Each language has its own culture, which causes cultural differences in meaning while translating words and texts. Cultural words, if not translated in an appropriate way in both the source language and target language (SL and TL), will cause misunderstanding between the two languages (literal translation). There is no full linguistic equivalent meaning in translating from SL into TL because of the cultural differences between the two languages. The translator who translates cultural words should be aware of the grammar and meaning and culture of the (SL and TL) to achieve a successful translation, and he should be aware of the techniques of equivalence and non-equivalence that have been used by scholars in the translation field. The research looks at the difficulties that translators go into when trying to translate terms in a descriptive-comparative analysis of these terms in source text (STs) and in target texts (TTs) in Media from English into Arabic. The research studies the useful translation strategies used in the translation of cultural (terms, concepts, idioms, and words) from English to Arabic for a glossary of words from BBC News and found out the quality of translation (accuracy, acceptability) for Arabic culture.

Buthaina Shaheen

One of the goals of second language acquisition research is to contribute to the development of a theory that can answer intriguing issues related to the role of first language in development and the extent to which universal principles of linguistic organization (universal grammar) guide the development of second language learners’ mental grammars for the target language. This study homes in on contributing to this goal by investigating how speakers of Lattakian Syrian Arabic acquire English indefinite RRCs. Based on the well-known properties of restrictive relative clauses in English, the account that best fits the data of English is the traditional operator movement analysis, while for Lattakian Syrian Arabic a clitic left-dislocation account offers the best fit. In this study, learners of different proficiency levels (as measured by an independent proficiency test) completed a grammaticality judgement task, a guided gap-filling task and a translation task. Results show partial first language influence at early stages of learning and persistent influence in later stages of learning, but specifically on properties that involve uninterpretable features. The findings largely support the theoretical position that argues for fundamental differences in native speaker and L2 syntactic representations. The implications of these findings for theories of second language acquisition are considered.

Banupriya Sivasubramaniyan, Aravind Banumathi Rajamanickam, Manjula Madhesan et al.

Technology integration in English language instruction has become essential in the digital age for the development of effective communication skills, particularly in writing. The effect of Digital Storytelling (DST) on improving the narrative writing abilities of BTech students learning English as a second language (ESL) is examined in this study. Based on metacognitive theory, which stresses learners'awareness and control of cognitive processes, the study investigates how DST promotes linguistic proficiency, creativity, and structured thinking. Two BTech student groups—the control and experimental groups—were evaluated during an eight-week intervention using a quasi-experimental design. The control group received traditional writing instruction, while the experimental group used multimodal tools like voiceovers, visuals, and narration scripts to complete DST tasks. Pre- and post-tests were given to evaluate students'progress in narrative writing using rubrics that emphasized vocabulary, grammar, coherence, organization, and creativity. The experimental group's narrative writing performance significantly improved, according to the findings, highlighting the contribution of DST to improving language proficiency, deeper engagement, and reflective thinking. By giving students a feeling of  purpose and ownership, the incorporation of digital storytelling not only enhanced the writing process but also inspired students. This study confirms that DST can be a transformative pedagogical tool in ESL contexts, especially for improving tertiary-level learners'narrative competencies. In order to improve student outcomes and encourage greater engagement with writing assignments, future implications recommend integrating DST into regular curricula.

Weixing Zhang, Jörg Holtmann, Daniel Strüber et al.

In model-driven engineering, developing a textual domain-specific language (DSL) involves constructing a meta-model, which defines an underlying abstract syntax, and a grammar, which defines the concrete syntax for the DSL. Language workbenches such as Xtext allow the grammar to be automatically generated from the meta-model, yet the generated grammar usually needs to be manually optimized to improve its usability. When the meta-model changes during rapid prototyping or language evolution, it can become necessary to re-generate the grammar and optimize it again, causing repeated effort and potential for errors. In this paper, we present GrammarOptimizer, an approach for optimizing generated grammars in the context of meta-model-based language evolution. To reduce the effort for language engineers during rapid prototyping and language evolution, it offers a catalog of configurable grammar optimization rules. Once configured, these rules can be automatically applied and re-applied after future evolution steps, greatly reducing redundant manual effort. In addition, some of the supported optimizations can globally change the style of concrete syntax elements, further significantly reducing the effort for manual optimizations. The grammar optimization rules were extracted from a comparison of generated and existing, expert-created grammars, based on seven available DSLs.

Santiago Herrera, Caio Corro, Sylvain Kahane

Descriptive grammars are highly valuable, but writing them is time-consuming and difficult. Furthermore, while linguists typically use corpora to create them, grammar descriptions often lack quantitative data. As for formal grammars, they can be challenging to interpret. In this paper, we propose a new method to extract and explore significant fine-grained grammar patterns and potential syntactic grammar rules from treebanks, in order to create an easy-to-understand corpus-based grammar. More specifically, we extract descriptions and rules across different languages for two linguistic phenomena, agreement and word order, using a large search space and paying special attention to the ranking order of the extracted rules. For that, we use a linear classifier to extract the most salient features that predict the linguistic phenomena under study. We associate statistical information to each rule, and we compare the ranking of the model's results to those of other quantitative and statistical measures. Our method captures both well-known and less well-known significant grammar rules in Spanish, French, and Wolof.

Sourojit Ghosh, Srishti Chatterjee

This chapter focuses on gender-related errors in machine translation (MT) in the context of low-resource languages. We begin by explaining what low-resource languages are, examining the inseparable social and computational factors that create such linguistic hierarchies. We demonstrate through a case study of our mother tongue Bengali, a global language spoken by almost 300 million people but still classified as low-resource, how gender is assumed and inferred in translations to and from the high(est)-resource English when no such information is provided in source texts. We discuss the postcolonial and societal impacts of such errors leading to linguistic erasure and representational harms, and conclude by discussing potential solutions towards uplifting languages by providing them more agency in MT conversations.

Lukas Galke, Limor Raviv

Finding and facilitating commonalities between the linguistic behaviors of large language models and humans could lead to major breakthroughs in our understanding of the acquisition, processing, and evolution of language. However, most findings on human-LLM similarity can be attributed to training on human data. The field of emergent machine-to-machine communication provides an ideal testbed for discovering which pressures are neural agents naturally exposed to when learning to communicate in isolation, without any human language to start with. Here, we review three cases where mismatches between the emergent linguistic behavior of neural agents and humans were resolved thanks to introducing theoretically-motivated inductive biases. By contrasting humans, large language models, and emergent communication agents, we then identify key pressures at play for language learning and emergence: communicative success, production effort, learnability, and other psycho-/sociolinguistic factors. We discuss their implications and relevance to the field of language evolution and acquisition. By mapping out the necessary inductive biases that make agents' emergent languages more human-like, we not only shed light on the underlying principles of human cognition and communication, but also inform and improve the very use of these models as valuable scientific tools for studying language learning, processing, use, and representation more broadly.

Daniel Ginting, Delli Sabudu, Yusawinur Barella et al.

Storytelling techniques serve as dynamic tools for enhancing language skills, encompassing both receptive (listening, reading) and productive (speaking, writing) proficiencies. In contrast to their non-narrative counterparts, these techniques offer a more potent array of teaching methodologies. This study aims to elucidate the current landscape of research concerning the efficacy of storytelling techniques. Initially, the investigation delves into the cognitive processing of narratives. Stories engage language processing centers, invigorate the visual cortex, evoke emotive responses, and facilitate comprehension of intricate information. Subsequently, the study explores linguistic processing theory and embodied cognition theory. Through an exhaustive literature review, this research applies a rigorous evidence synthesis method to assess selected studies, culminating in the amalgamation of findings when comparability permits. The study unveils that storytelling techniques foster reading and listening comprehension, bolster speaking and writing skills, and kindle creativity and imagination. Moreover, enhancements span vocabulary, grammar, and syntax. Nonetheless, while generally efficacious, the uniformity of effectiveness across diverse learners remains a nuanced aspect.

Saima aslam

<!--StartFragment--> <p class="MsoNormal" style="margin-top: 0cm; margin-right: 43.2pt; margin-bottom: 0cm; margin-left: 43.2pt; text-align: justify; line-height: normal;"><span style="font-size: 12.0pt; font-family: &quot;Times New Roman&quot;,serif;" lang="EN-US"><span style="mso-spacerun: yes;">&nbsp;</span>Eco-criticism is relatively considered a new term in Urdu literature although a brief review of Urdu literature particularly poetry indicates that environmental problems have always been critically observed by the literary minds. Ecocriticism is a kind of literary criticism that discuss the relationship of the physical environment as a living entity with literature and not just as a background of social dialogue. It is a theoretical technique that was introduced in the 90s in the west, however, Imdad Imam Asar has been considered the earliest critic who used this technique in Urdu literature through his book &ldquo;KashifulHaqaiq&rdquo;. Afterwards, Maulvi Muhammad Ismail Merathi used ecological perspectives in their poetry at a time when ecological studies and organizations had yet not been established. Dr. Wazir Agha, Nasir Kazmi, Majeed Amjad, Parveen Shakir and Tariq Naeem pronounced the disturbing effects of deforestation on nature, particularly on birds. Urdu poets mourned the high industrial ambitions of humans which destroyed the environmental beauty and awarded air pollution, global warming, climate change and drought to nature and humans. Poets like Mustafa Zaidi, Zia Jalandhari and others believed that environmental destruction is not limited to the physical aspects only it also disturbed the social behavior of the humans. Some modern poets like KishwarNaheed, Yousaf Zafar and Saeed Aasi associated the decay of humanity with environmental change in their poems and warns humans against it. </span></p> <!--EndFragment-->

Yanzhu Guo, Guokan Shang, Michalis Vazirgiannis et al.

This study investigates the consequences of training language models on synthetic data generated by their predecessors, an increasingly prevalent practice given the prominence of powerful generative models. Diverging from the usual emphasis on performance metrics, we focus on the impact of this training methodology on linguistic diversity, especially when conducted recursively over time. To assess this, we adapt and develop a set of novel metrics targeting lexical, syntactic, and semantic diversity, applying them in recursive finetuning experiments across various natural language generation tasks in English. Our findings reveal a consistent decrease in the diversity of the model outputs through successive iterations, especially remarkable for tasks demanding high levels of creativity. This trend underscores the potential risks of training language models on synthetic text, particularly concerning the preservation of linguistic richness. Our study highlights the need for careful consideration of the long-term effects of such training approaches on the linguistic capabilities of language models.