Search Results (386)

In the context of the BiblIndex project, which is an online index of biblical textual reuses by the Church Fathers, intrabiblical intertextuality must be considered to better understand the underlying basis of the Church Fathers’ thought. This paper examines the reuse of Psalmic texts in the New Testament as a test case for experimenting with a detection tool that uses traditional natural language processing (NLP) methods exclusively. Biblical verses are compared using similarity measures based on various NLP operations, such as tokenization, lemmatization, part-of-speech tagging, stop word filtering and synset assignment. Textometric measures provide a framework for the numerical assessment of grammatical, lexical and semantic similarities between textual units. We demonstrate the efficiency of this reproducible method, which does not involve a ‘black box’ effect, for detecting and characterizing literal quotations and a significant range of echoes. Full article

Background/Objectives: Hearing impairment in middle-aged adults is a significant, modifiable risk factor for cognitive decline and dementia, and therapy with hearing aids or cochlear implants has been suggested to reduce this risk. However, most research on auditory rehabilitation and cognition has focused on older adults, and evidence regarding cognitive outcomes in middle-aged adults remains scarce despite this group being identified as critical for dementia prevention. Thus, this study aimed to assess cognitive skills in middle-aged hearing-impaired individuals 1 year after receiving a cochlear implant (CI) as part of auditory rehabilitation. Methods: Thirty-two patients with a mean age of 52.4 were enrolled in a prospective pre-post study. Hearing was tested using the Freiburg Monosyllable Test (FS) and the Oldenburg Inventory (OI). Cognitive performance was assessed using the WAIS-IV, operationalized through the Working Memory Index (Digit Span, Arithmetic) and Processing Speed Index (Symbol Search, Coding). Quality of life was assessed with the NCIQ, tinnitus-related distress with the Tinnitus Questionnaire (TQ), and depressive symptoms with the ADS-L. Results: After one year, speech intelligibility (FS) improved from a median of 0 to 70.0 (Wilcoxon Z = −4.864, p < 0.001, r = −0.61), and subjective hearing from a median of 2.55 to 3.18 (Wilcoxon Z = −3.072, p = 0.002). The NCIQ score increased from 52.3 to 60.6 (Z = −3.899, p < 0.001), and tinnitus-related distress decreased from 25 to 21 (Wilcoxon Z = −2.209, p = 0.027). Depressive symptoms declined numerically, although this change did not reach statistical significance. Working memory improved from 82.0 to 89.0 (Wilcoxon Z = −4.090, p < 0.001), and processing speed from 89.5 to 95.5 (Wilcoxon Z = −2.533, p = 0.011). Before CI, WMI and PSI showed a strong correlation (ρ = 0.533, p = 0.002), and WMI correlated moderately with education level (ρ = 0.452, p = 0.012). One year after CI, correlations strengthened between PSI and NCIQ (ρ = 0.510, p = 0.006), PSI and OI (ρ = 0.400, p = 0.039), and WMI and TQ (ρ = –0.459, p = 0.021), indicating emerging associations between cognitive outcomes and auditory or psychosocial measures. Conclusions: One year of CI-based auditory rehabilitation improves auditory function, quality of life, tinnitus distress, and—critically—working memory and processing speed in middle-aged adults. These findings address a previously unfilled research gap and support the relevance of CIs for preserving cognitive health during midlife. Full article

“After this I will return, and I will rebuild the dwelling of David, which has fallen; from its ruins I will rebuild it, and I will set it up” (Acts 15:16 NRSV). This verse, quoting Amos 9:11, is part of James’ speech to the Jerusalem “council” considering the issue of Gentile believers and their relationship to the Law. In some Pentecostal circles, especially those influenced by the “Latter Rain” revival of 1948, this verse has taken on a different force based on the Greek skēnē, literally a tent or traditionally “tabernacle.” This teaching is based on the “tabernacle of David” as described in 1 Chron. 16:1–6 and other places and likely the venue in which some of the original psalms were performed. Their argument is that this is part of a model for experiencing the dynamic presence of God in worship and the restoration of the NT church. It forms a theological basis or rationale for contemporary praise and worship with the use of musical instruments, lifted hands, dance, clapping, etc., following the Psalms. However, this interpretation of “David’s fallen tent” seems to fail interpretive tests such as context, authorial intention and audience understanding. This article discusses the interpretive challenges raised by Acts 15:16, how skilled interpreters have understood the “tent of David,” and some weaknesses in these readings. The argument of “Latter Rain” authors on the “Tabernacle of David” is explained and evaluated in the light of some other contemporary research and hermeneutical principles associated with typology. The Latter Rain position is found to have considerable strengths but overreaches in some key points especially by making structure too central. Full article

In recent years, robots have started to be used in tasks involving human interaction. For this to be possible, humans must perceive robots as suitable interaction partners. This can be achieved by giving the robots an animate appearance. One of the methods that can be utilised to endow a robot with a lively appearance is giving it the ability to perform expressions on its own, that is, combining multimodal actions to convey information. However, this can become a challenge if the robot has to use gestures and speech simultaneously, as the non-verbal actions need to support the message communicated by the verbal component. In this manuscript, we present a system that, based on a robot’s utterances, predicts the corresponding gesture and synchronises it with the speech. A deep learning-based prediction model labels the robot’s speech with the types of expressions that should accompany it. Then, a rule-based synchronisation module connects different gestures to the correct parts of the speech. For this, we have tested two different approaches: (i) using a combination of recurrent neural networks and conditional random fields; and (ii) using transformer models. The results show that the proposed system can properly select co-speech gestures under the time constraints imposed by real-world interactions. Full article

The automatic construction of learners’ dictionaries requires robust methods for identifying non-literal word combinations, or collocations, which represent a significant challenge for second-language (L2) learners. This paper addresses the critical initial step of accurately extracting collocation candidates from corpora to build a learner’s dictionary for Italian. The adopted method and the implemented application are significant for learning the Italian language. We present a comparative study of three methodologies for identifying these candidates within a 41.7-million-word Italian corpus: a Part-Of-Speech-based approach, a syntactic dependency-based approach, and a novel Hybrid method that integrates both. The analysis yielded 2,097,595 potential collocations. Results indicate that the Hybrid method achieves superior performance in terms of Recall and Benchmark Match, identifying the most significant portion of candidates, 42.35% of the total. We conducted an in-depth analysis to refine the extracted dataset, calculating multiple statistical metrics for each candidate, which are described in detail in the paper. Such analysis allows for the classification of collocations by relevance, difficulty, and frequency of use, forming the basis for the future development of a high-quality, web-based dictionary tailored to the proficiency levels of Italian learners. Full article

The assessment of Chinese text readability plays a significant role in Chinese language education. Due to the intrinsic differences between alphabetic languages and Chinese character representations, the readability assessment becomes more challenging in terms of the language’s inherent complexity in vocabulary, syntax, and semantics. The article proposed the conceptual analogy between Chinese readability assessment and music’s rhythm and tempo patterns, in which the syntactic structures of the Chinese sentences could be transformed into an image. The Chinese Knowledge and Information Processing Tagger (CkipTagger) tool developed by Sinica-Taiwan is utilized to decompose the Chinese text into a set of tokens. These tokens are then refined through a user-defined token pool to retain meaningful units. An image with part-of-speech (POS) information will be generated by using the token versus syntax alignment. A discrete cosine transform (DCT) is then applied to extract the temporal characteristics of the text. Moreover, the study integrated four categories: linguistic features–type–token ratio, average sentence length, total word, and difficulty level of vocabulary for the readability assessment. Finally, these features were fed into the Support Vector Machine (SVM) network for the classifications. Furthermore, a bidirectional long short-term memory (Bi-LSTM) network is adopted for quantitative comparisons. In simulation, a total of 774 Chinese texts fitted with Taiwan Benchmarks for the Chinese Language were selected and graded by Chinese language experts, consisting of equal amounts of basic, intermediate, and advanced levels. The finding indicated the proposed POS with the linguistic features work well in the SVM network, and the performance matches with the more complex architectures like the Bi-LSTM network in Chinese readability assessments. Full article

The brain in healthy adults shows language localization in the left hemisphere, and the evidence from the literature supports neural plasticity after traumatic injuries. What happens if an injury occurs early in brain development? How does early unilateral brain damage affect a child’s ability to acquire language? Evidence regarding language development after early unilateral brain damage is mixed. Therefore, this case report aims to present the language and cognitive status at the age of seven in a child who suffered a left-sided arterial presumed perinatal ischemic stroke (APPIS), with reference to her MRI findings. As part of her ongoing rehabilitation, she has received continuous speech therapy since age four and physiotherapy since six months of age. The current evaluation provides insights into long-term neurodevelopmental outcomes following early brain injury, highlighting the variability in clinical outcomes and considering the potential for functional restitution. Full article

This paper investigates language use in interspecies interaction. The aim is to characterize the variation in children’s pet-oriented speech and to explore how semantic complexity is treated. The analysis first presents quantitative observations on properties of talk and the ongoing activities. The dataset includes 19 video recordings presenting cats, dogs, and 6–12-year-old French-speaking children in an ecological context. The analysis showed that children spoke more to dogs than to cats. We also found a strong correlation between play activity and the amount of talk. We then observed that, just like adults in the previous literature, children use short utterances when talking to pets. This may indicate syntactic simplification. The second part of the study concentrated on complex referential constructions in two child–dog play sequences. It showed that the child enacted multilayered semantic configurations through indexical embodied means. The dog participated in constructing the referential link when she was asked to choose between alternatives. When it came to locating an absent referent, the child continued to employ indexical signs to support the abstract semantic structure, displaying awareness of the participants’ different semiotic worlds. The paper supports that while syntax is simplified, meaning structures may remain complex and become part of the play. Full article

The performance of extractive summarization models is often limited by their dependence on human references that may contain inaccuracies or subjective biases. Existing methods typically rely solely on sentence-level supervision, which lacks explicit grounding in the actual semantic content of the source document, thus limiting their robustness. We propose SSF-KW, a novel multi-task learning framework that enhances robustness by jointly optimizing keyword extraction and sentence selection. Our approach is designed to explicitly anchor salience decisions in the document’s intrinsic semantic structure, reducing reliance on potentially noisy labels. To this end, the model employs a shared BERT encoder to represent sentences, and identifies keywords through part-of-speech tagging, semantic similarity analysis, and fine-grained keyword signals with sentence-level representations via a transformer-based fusion module. The entire framework is optimized with a combined loss function that balances both tasks. Comprehensive evaluations on CNN/DailyMail, XSum, and WikiHow demonstrate that SSF-KW consistently outperforms baselines ROUGE-1 scores of 43.27, 25.43, and 30.03, respectively. Ablation studies confirm the contribution of each component, with the word-level module proving especially critical for capturing key concepts in procedural texts like WikiHow. Full article

This study presents a comprehensive NLP infrastructure for Modern Greek that bridges grammatical analysis and ontological representation, integrating linguistic theory, algorithmic modeling, and semantic structuring within a unified computational framework. Modern Greek remains under-resourced in terms of interoperable linguistic tools, with existing systems addressing morphology, syntax, or semantics separately. The proposed system fills this gap by connecting data extraction, morphological generation, and syntactic analysis within a single executable environment, validated through diagnostic evaluation and ontology consistency checking. Linguistic data were automatically retrieved from the Dictionary of Standard Modern Greek (55,101 entries), processed through Python-based algorithms, and integrated into an ontology in Protégé. The resulting ontology captures grammatical and semantic relations with high precision and supports reasoning, interoperability, and linguistic knowledge expansion. The system transforms grammatical knowledge into ontological knowledge, providing a scalable linguistic foundation for future advancements in Natural Language Processing and Understanding for Modern Greek. Full article

Parameter-efficient fine-tuning (PEFT) methods like Low-Rank Adaptation (LoRA) are widely used to adapt large pre-trained models under limited resources, yet they often underperform full fine-tuning in low-resource automatic speech recognition (ASR). This gap stems partly from initialization strategies that ignore speech signals’ inherent spectral-phase structure. Unlike SVD/QR-based approaches (PiSSA, OLoRA) that construct mathematically optimal but signal-agnostic subspaces, we propose SPaRLoRA (Spectral-Phase Residual LoRA), which leverages Discrete Fourier Transform (DFT) bases to create speech-aware low-rank adapters. SPaRLoRA explicitly incorporates both magnitude and phase information by concatenating real and imaginary parts of DFT basis vectors, and applies residual correction to focus learning exclusively on components unexplained by the spectral subspace. Evaluated on a 200-h Sichuan dialect ASR benchmark, SPaRLoRA achieves a 2.1% relative character error rate reduction over standard LoRA, outperforming variants including DoRA, PiSSA, and OLoRA. Ablation studies confirm the individual and complementary benefits of spectral basis, phase awareness, and residual correction. Our work demonstrates that signal-structure-aware initialization significantly enhances parameter-efficient fine-tuning for low-resource ASR without architectural changes or added inference cost. Full article

Arabic natural language processing (NLP) has garnered significant attention in recent years due to the growing demand for automated text and Arabic-based intelligent systems, in addition to digital transformation in the Arab world. However, the unique linguistic characteristics of Arabic, including its rich morphology, diverse dialects, and complex syntax, pose significant challenges to NLP researchers. This paper provides a comprehensive review of the main linguistic challenges inherent in Arabic NLP, such as morphological complexity, diacritics and orthography issues, ambiguity, and dataset limitations. Furthermore, it surveys the major computational techniques employed in tokenisation and normalisation, named entity recognition, part-of-speech tagging, sentiment analysis, text classification, summarisation, question answering, and machine translation. In addition, it discusses the rapid rise of large language models and their transformative impact on Arabic NLP. Full article

Background: Preterm infants are highly fragile and at increased risk of developing Cerebral Palsy (CP). Therefore, early detection through an interdisciplinary approach is necessary to enable timely referrals and evidence-based interventions. The literature recommends the use of the Hammersmith Infant Neurological Examination (HINE), the WHOQOL-BREF quality of life questionnaire, and the Comprehensive Neonatal Speech-Language Assessment Protocol (EFIN) for early CP diagnosis. However, despite the availability of these tools, they have not yet been implemented as part of evaluation and follow-up protocols in Colombia. Methods: A cross-sectional observational and analytical study was conducted to analyze, in a group of preterm infants, the relationship between neurological risk, primary stomatognathic functions (suction-swallowing-breathing), and caregivers’ perceived quality of life. Results: A total of 43 preterm infants were included. Of these, only 9.30% showed neurological risk; 97.67% did not present alterations in the suction-swallowing-breathing triad; and the lowest quality of life scores were reported in social relationships and psychological health. Conclusions: There are perinatal factors that require follow-up in preterm infants to prevent possible future complications. It is essential to address both social and psychological aspects in family support programs. Full article

There are close relationships between vertical positions in space and auditory experiences of fundamental frequency (F0), which even very young children seem to use. Like adults, they associate higher or rising F0 values with upper positions in space and vice versa for lower or falling F0 values. Here, we tested whether caregivers’ speech capitalizes on these associations to guide the attention of very young children (18 to 24 months). Together with their child, caregivers saw four different objects (in the corners of a computer screen). Caregivers produced standardized sentences (e.g., “Look at the hat.”). We observed a significantly increased maximum F0 (but no differences in mean or minimum F0) at the onset of the utterance when the to be named object appeared at an upper position compared to the same object at a lower position. Furthermore, exploratory analyses of F0 dynamics of caregivers’ utterances indicated that the higher F0 maximum was part of a more prominent F0 increase for objects appearing at an upper compared to a lower position. Adults without a child present and less experience in interacting with children did not show systematic pitch modulation when addressing an imagined child in the same study set-up. Thus, caregivers appear to systematically modulate F0 dynamics to provide an effective language environment when they are interacting with their child. Full article

Part-of-speech (POS) tagging in low-resource, morphologically rich languages (LRLs/MRLs) remains challenging due to extensive affixation, high out-of-vocabulary (OOV) rates, and pervasive polysemy. We propose MRL-POS, a unified Transformer-CRF framework that dynamically selects informative affix features and integrates them with deep contextual embeddings via a novel dual-gating co-attention mechanism. First, a Dynamic Affix Selector adaptively adjusts n-gram ranges and frequency thresholds based on word length to ensure high-precision affix segmentation. Second, the Affix–Context Co-Attention Module employs two gating functions that conditionally amplify contextual dimensions with affix cues and vice versa, enabling robust disambiguation of complex and ambiguous forms. Third, Layer-Wise Attention Pooling aggregates multi-layer XLM-RoBERTa representations, emphasizing those most relevant for morphological and syntactic tagging. Evaluations on Uyghur, Kyrgyz, and Uzbek show that MRL-POS achieves an average F₁ of 84.10%, OOV accuracy of 84.24%, and Poly-F₁ of 72.14%, outperforming strong baselines by up to 8 F₁ points. By explicitly modeling the symmetry between morphological affix cues and sentence-level context through a dual-gating co-attention mechanism, MRL-POS achieves a balanced fusion that both preserves local structure and captures global dependencies. Interpretability analyses confirm that 89.1% of the selected affixes align with linguistic expectations. This symmetric design not only enhances robustness in low-resource and agglutinative settings but also offers a general paradigm for symmetry-aware sequence labeling tasks. Full article