Search Results (97)

The global expansion of Korean language education has created an urgent need for scalable, objective, and consistent methods for assessing the writing skills of non-native (L2) learners. Traditional manual grading is resource-intensive and prone to subjectivity, while existing Automated Essay Scoring (AES) systems often struggle with the linguistic nuances of Korean and the specific error patterns of L2 writers. This paper introduces a novel hybrid AES system designed specifically for Korean L2 writing. The system integrates two complementary feature sets: (1) a comprehensive suite of conventional linguistic features capturing lexical diversity, syntactic complexity, and readability to assess writing form and (2) a novel semantic relevance feature that evaluates writing content. This semantic feature is derived by calculating the cosine similarity between a student’s essay and an ideal, high-proficiency reference answer generated by a Large Language Model (LLM). Various machine learning models are trained on the Korean Language Learner Corpus from the National Institute of the Korean Language to predict a holistic score on the 6-level Test of Proficiency in Korean (TOPIK) scale. The proposed hybrid system demonstrates superior performance compared to baseline models that rely on either linguistic or semantic features alone. The integration of the LLM-based semantic feature provides a significant improvement in scoring accuracy, more closely aligning the automated assessment with human expert judgments. By systematically combining measures of linguistic form and semantic content, this hybrid approach provides a more holistic and accurate assessment of Korean L2 writing proficiency. The system represents a practical and effective tool for supporting large-scale language education and assessment, aligning with the need for advanced AI-driven educational technology systems. Full article

This study investigates how Mexican Spanish/U.S. English heritage bilinguals process scope ambiguities in sentences containing the existential quantifiers a/una and the universal quantifiers every/cada in English and Spanish. Sentences like ‘A person bought every book’ are syntactically ambiguous in both languages, allowing for multiple possible interpretations. Research suggests that one interpretation is often preferred due to lower cognitive demand, though degree of preference varies across languages. Notably, heritage bilinguals may have distinct interpretation preferences in each language, highlighting the complexity of bilingual processing. Sixty Spanish/English heritage bilinguals (Age M = 25.48, SD = 2.65) completed a timed and graded truth-value judgment task in both languages, along with language proficiency tests. We analyzed interpretation ratings, response times, and potential effects of proficiency. Results reveal nearly identical preferred interpretation ratings (Spanish: M = 4.19, SD = 0.56; English: M = 4.14, SD = 0.66) and response times (Spanish: M = 6.97 s, SD = 2.70; English: M = 6.67 s, SD = 1.80) across languages, with one interpretation consistently favored and associated with faster response times. Language proficiency had no significant impact. Our experimental findings offer new insights into heritage bilinguals’ processing of competing linguistic structures and inform models of bilingual syntax and cognitive flexibility. Full article

Boat collisions pose severe threats to maritime safety, economic activity, and environmental sustainability. Conventional risk assessment methods—such as Failure Mode and Effects Analysis, and Fault Tree Analysis—are widely applied but remain inadequate for addressing the uncertainty, subjectivity, and interdependency of risk factors in complex maritime environments. This study proposes a fuzzy Multi-Criteria Decision-Making framework for the risk assessment of boat collisions. The model integrates fuzzy logic with Analytic Hierarchy Process for criterion weighting and the Technique for Order Preference by Similarity to the Ideal Solution for risk ranking. Fuzzy logic is employed to capture linguistic expert judgments and to manage vague or incomplete data, which are common challenges in marine operations. Key collision risk factors—human error, boat engine system failure, environmental conditions, and intentional threats—are identified through literature review, incident data analysis, and expert consultation. A comparative analysis with a baseline non-fuzzy model demonstrates the added value of the fuzzy-integrated framework, showing improved capacity to handle imprecision and uncertainty. The model outputs not only prioritise risk rankings but also support the identification of critical control actions and effective safety measures. A case study of Nigerian waters illustrates the practicality of the framework in guiding risk mitigation strategies and informing policy decisions under uncertainty. Full article

In real-world decision-making situations, experts often rely on subjective and imprecise judgments, frequently expressed using linguistic terms. While fuzzy logic offers a valuable tool to capture and process such uncertainty, traditional methods often convert fuzzy inputs into crisp values too early in the process. This premature defuzzification can result in significant loss of information and reduced interpretability. To address this issue, the present study introduces an enhanced fuzzy TOPSIS model that utilizes expected interval representations instead of early crisp transformation. This approach allows the original fuzzy data to be preserved throughout the analysis, leading to more transparent, realistic, and informative decision outcomes. The practical application of the proposed method is demonstrated through a supplier selection case study, which illustrates the model’s capability to handle real-world, complex, and qualitative decision environments. By explicitly linking the method to this domain, the study provides a concrete anchor for practitioners and decision-makers seeking transparent and robust evaluation tools. Full article

Strategic fleet renewal represents a fundamental challenge for small and medium-sized enterprises (SMEs) and public entities seeking to align their operational objectives with sustainable mobility practices. This paper proposes a hybrid decision support model based on fuzzy logic, combining the Fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method with the Fleet Renewal Priority Index (FRPI). The model evaluates and prioritizes different vehicle alternatives based on multiple economic, environmental, and operational criteria, including total cost of operation, CO₂ emissions, maintenance, autonomy, infrastructure compatibility, and energy independence. The criteria are evaluated by linguistic judgments converted into triangular fuzzy numbers (TFN), allowing uncertainty and subjectivity to be addressed. A simulated case study illustrates the application of the model, identifying the vehicles most aligned with a sustainability and efficiency strategy, as well as those that present a greater urgency for replacement. The results demonstrate the potential of the approach to support rational, transparent and sustainable decisions in fleet modernization. Full article

Objectives: This study aimed to evaluate the performance of three widely used artificial intelligence (AI) chatbots—ChatGPT-4, Gemini 2.5 Pro, and Claude Sonnet 4—in answering patient-oriented frequently asked questions (FAQs) related to orthognathic surgery. Given the increasing reliance on AI tools in healthcare, it is essential to evaluate their performance to provide accurate, empathetic, readable, and clinically appropriate information. Methods: Twenty FAQs in Turkish about orthognathic surgery were presented to each chatbot. The responses were evaluated by three oral and maxillofacial surgeons using a modified Global Quality Score (GQS), binary clinical appropriateness judgment, and a five-point empathy rating scale. The evaluation process was conducted in a double-blind manner. The Ateşman Readability Formula was applied to each response using an automated Python-based script. Comparative statistical analyses—including ANOVA, Kruskal–Wallis, and post hoc tests—were used to determine significant differences in performance among chatbots. Results: Gemini outperformed both GPT-4 and Claude in GQS, empathy, and clinical appropriateness (p < 0.001). GPT-4 demonstrated the highest readability scores (p < 0.001) but frequently lacked empathetic tone and safety-oriented guidance. Claude showed moderate performance, balancing ethical caution with limited linguistic clarity. A moderate positive correlation was found between empathy and perceived response quality (r = 0.454; p = 0.044). Conclusions: AI chatbots vary significantly in their ability to support surgical patient education. While GPT-4 offers superior readability, Gemini provides the most balanced and clinically reliable responses. These findings underscore the importance of context-specific chatbot selection and continuous clinical oversight to ensure safe and ethical AI-driven communication. Full article

This paper investigates the acceptability of focused Objects with [+corrective, +exhaustive] features in Italian and French, considering the role of syntactic rigidity, Exhaustivity Markers (EMs), and argument structure. We conducted two parallel acceptability judgment experiments (one per language), testing focused Objects in three positions: (i) in situ, (ii) fronted (FF), and (iii) clefted (CC). Each sentence was also presented with and without an explicit EM, and the verb type was controlled across three categories: transitive, unergative, and unaccusative verbs. Results reveal key cross-linguistic differences: (i) FF is the least acceptable strategy in both languages, contradicting the assumption that Italian tolerates FF more than French; (ii) Italian speakers prefer in situ Focus with an explicit EM, whereas French speakers rate in situ and CC Focus equally acceptable, favoring implicit exhaustivity; (iii) verb type does not significantly impact Focus acceptability, except in French, where intervention effects may reduce FF acceptability in transitive/unergative contexts; (iv) CC remains a viable alternative to in situ Focus in French, possibly acting as a repair strategy. These findings suggest that, as far as [+corrective, +exhaustive] Focus is concerned, Italian does not appear to be less syntactically rigid than French. Full article

This paper investigates transgenerational change in the use of formal address forms among Polish heritage speakers in Germany by analyzing their language attitudes and usage preferences. The survey-based study involved 100 bilingual Polish speakers with a migration background, including both late and early immigrants vs. representatives of the first and second generations, respectively. The survey included two parts: (1) a questionnaire assessing language attitudes toward formal address systems in Polish and German, respectively, and (2) an Acceptability Judgment Task evaluating respondents’ preferences for different address variants, including contact-induced hybrid forms, in simulated communicative situations. By comparing language attitudes and usage preferences among heritage speakers, the study seeks to identify mechanisms of transgenerational change in pragmatics of their heritage language. The findings reveal a discrepancy between language attitudes and actual language use by heritage speakers. While respondents recognize asymmetries between Polish and German formal address systems, their usage preferences align predominantly with the Polish monolingual norm, particularly in perceptually oriented tasks. However, the emergence of hybrid forms of formal address suggests a gradual shift toward increased tolerance and acceptance of contact-induced variations. This finding supports the hypothesis that pragmatics, like other linguistic levels, undergoes a transgenerational shift in migration settings, with language attitudes serving as earlier indicators of change. Full article

Ironic remarks are often pronounced with a distinctive intonation. It is not clear whether children rely on acoustic cues to attribute an ironic intent. This question has been only indirectly tackled, with studies that manipulated the intonation with which the final remark is pronounced within an irony comprehension task. We propose a new task that is meant to assess whether children rely on prosody to infer speakers’ sincere or ironic communicative intentions, without requiring meta-linguistic judgments (since pragmatic awareness is challenging for young children). Children listen to evaluative remarks (e.g., “That house is really beautiful”), pronounced with sincere or ironic intonation, and they are asked to identify what the speaker is referring to by selecting one of two pictures depicting an image corresponding to a literal interpretation (a luxury house) and one to its reverse interpretation (a hovel). We tested eighty children aged 3 to 11 years and found a clear developmental trend, with children consistently responding above the chance level from age seven, and there was no correlation with the recognition of emotions transmitted through the vocal channel. Full article

Incident prioritization is a critical task in enterprise environments, where textual descriptions of service disruptions often contain vague or ambiguous language. Traditional machine learning models, while effective in rigid classification, struggle to interpret the linguistic uncertainty inherent in natural language reports. This paper proposes a fuzzy logic-based framework for incident categorization and prioritization, integrating natural language processing (NLP) with a formal system of fuzzy inference. The framework transforms semantic embeddings from incident reports into fuzzy sets, allowing incident severity and urgency to be represented as degrees of membership in multiple categories. A mathematical model based on Mamdani-type inference and triangular membership functions is developed to capture and process imprecise inputs. The proposed system is evaluated on a real-world dataset comprising 10,000 incident descriptions from a mid-sized technology enterprise. A comparative evaluation is conducted against two baseline models: a fine-tuned BERT classifier and a traditional support vector machine (SVM). Results show that the fuzzy logic approach achieves a 7.4% improvement in F1-score over BERT (92.1% vs. 85.7%) and a 12.5% improvement over SVM (92.1% vs. 79.6%) for medium-severity incidents, where linguistic ambiguity is most prevalent. Qualitative analysis from domain experts confirmed that the fuzzy model provided more interpretable and context-aware classifications, improving operator trust and alignment with human judgment. These findings suggest that fuzzy modeling offers a mathematically sound and operationally effective solution for managing uncertainty in text-based incident management, contributing to the broader understanding of mathematical modeling in enterprise-scale social phenomena. Full article

The cognitive core of human beings is closely connected to the concept of animacy, which significantly influences their memory, vision, and complex language comprehension. While animacy is reflected in language through subtle constraints on verbs and adjectives, it is also acquired and honed through non-linguistic experiences. In the same vein, we suggest that the limited capacity of LLMs to grasp natural language, particularly in relation to animacy, stems from the fact that these models are trained solely on textual data. Hence, the question this paper aims to answer arises: Can LLMs, in their digital wisdom, process animacy in a similar way to what humans would do? We then propose a systematic analysis via prompting approaches. In particular, we probe different LLMs using controlled lexical contrasts (animate vs. inanimate nouns) and narrative contexts in which typically inanimate entities behave as animate. Results reveal that, although LLMs have been trained predominantly on textual data, they exhibit human-like behavior when faced with typical animate and inanimate entities in alignment with earlier studies, specifically on seven LLMs selected from three major families—OpenAI (GPT-3.5, GPT-4), Meta (Llama2 7B, 13B, 70B), and Mistral (Mistral-7B, Mixtral). GPT models generally achieve the most consistent and human-like performance, and in some tasks, such as sentence plausibility and acceptability judgments, even surpass human baselines. Moreover, although to a lesser degree, the other models also assume comparable results. Hence, LLMs can adapt to understand unconventional situations by recognising oddities as animated without needing to interface with unspoken cognitive triggers humans rely on to break down animations. Full article

The adverb anymore is standardly a negative polarity item (NPI), which must be licensed by triggers of non-positive polarity. Some Englishes also allow anymore in positive-polarity clauses. Linguists have posited that this non-polarity anymore (NPAM) carries a feature of negative affect. However, this claim is based on elicited judgments, and linguists have argued that respondents cannot reliably evaluate NPAM via conscious judgment. To solve this problem, we employ sentiment analysis to examine the relationship between NPAM and negative affect in a Twitter corpus. Using two complementary sentiment analytic frameworks, we demonstrate that words occurring with NPAM have lower valence, higher arousal, and lower dominance than words occurring with NPI-anymore. Broadly, this confirms NPAM’s association with negative affect in natural-language productions. We additionally identify inter- and intra-regional differences in affective dimensions, as well as variability across different types of NPI trigger, showing that the relationship between negative affect and NPAM is not monolithic dialectally, syntactically, or semantically. The project demonstrates the utility of sentiment analysis for examining emotional characteristics of low-frequency variables, providing a new tool for dialectology, micro-syntax, and variationist sociolinguistics. Full article

Natural disasters and emergencies continue to increase in frequency and severity worldwide, necessitating robust emergency management (EM) systems and evaluation methodologies. This study addresses critical gaps in current emergency response capacity (ERC) evaluation frameworks by developing a comprehensive quantitative decision-making model to assess ERC more effectively. This research constructs a systematic ERC assessment framework based on the four phases of the disaster management cycle (DMC): prevention, preparedness, response, and recovery. The methodology employs multi-criteria decision analysis to evaluate ERC using three distinct information representation environments: intuitionistic fuzzy (IF) sets, linguistic variables (LV), and a novel mixed IF-LV environment. For each environment, we derive appropriate aggregation operators, weight determination methods, and information fusion mechanisms. The proposed model was empirically validated through a case application to emergency plan selection in Shenzhen, China. A statistical analysis of results demonstrates high consistency across all three decision environments (IF, LV, and mixed IF-LV), confirming the model’s robustness and reliability. A sensitivity analysis of key parameters further validates the model’s stability. Results indicate that the proposed decision-making approach provides significant value for EM by enabling more objective, comprehensive, and flexible ERC assessment. The indicator system and evaluation methodology offer decision-makers (DMs) tools to quantitatively analyze ERC using various information expressions, accommodating both subjective judgments and objective metrics. This framework represents an important advancement in emergency preparedness assessment, supporting more informed decision-making in emergency planning and response capabilities. Full article

Subject pronoun expression (SPE) has been widely studied as a sociolinguistic variable across a range of languages. However, previous research has primarily focused on production, leaving the perception of subject pronouns largely unexplored. The perception of sociolinguistic variants not only reflects unconscious judgments towards linguistic features but also unveils the social meanings associated with these features. This study explores the perception of SPE in Mandarin by native and non-native listeners. 262 participants (185 native and 77 non-native) were recruited for Mandarin SPE perception tasks in which participants needed to rate the appropriate use of SPE in given contexts. Mixed-effects regression models reveal that native and non-native Mandarin listeners shared similar perception patterns of SPE. SPE rate serves as a significant structural constraint for both native and non-native perception. However, these two groups differ in that person of the subject and L2 experience play a key role in native perception, whereas non-native listeners demonstrated greater sensitivity to gender as a social factor. To what extent production and perception may interact is further discussed. This study contributes to the current understanding of sociolinguistics and second language acquisition by providing empirical evidence of SPE perception, adopting innovative approaches to examine variation perception, addressing the differences between native and non-native patterns of perceptual variation, and exploring the connection between variation production and perception. Full article

Music has been shown to increase arousal and attention and even facilitate processing during non-musical tasks, including those related to speech and language functions. Mind wandering has been studied in many sustained attention tasks. Here, we investigated the intersection of these two phenomena: the role of mind wandering while listening to familiar/unfamiliar musical excerpts, and its effects on concurrent linguistic processing. We hypothesized that familiar music would be less distracting than unfamiliar music, causing less mind wandering, and consequently benefit concurrent speech perception. Participants (N = 96 young adults) performed a lexical-semantic congruity task where they judged the relatedness of visually presented word pairs while listening to non-vocal classical music (familiar or unfamiliar orchestral pieces), or a non-music environmental sound clip (control) played in the background. Mind wandering episodes were probed intermittently during the task by explicitly asking listeners if their mind was wandering in that moment. The primary outcome was accuracy and reactions times measured during the lexical-semantic judgment task across the three background music conditions (familiar, unfamiliar, and control). We found that listening to familiar music, relative to unfamiliar music or environmental noise, was associated with faster lexical-semantic decisions and a lower incidence of mind wandering. Mind wandering frequency was similar when performing the task when listening to familiar music and control environmental sounds. We infer that familiar music increases task enjoyment, reduces mind wandering, and promotes more rapid lexical access during concurrent lexical processing, by modulating task-related attentional resources. The implications of using music as an aid during academic study and cognitive tasks are discussed. Full article