Language Processing Research Articles

The Issues and Optimization Strategies in the Modernization of Vocational Education in the Era of Chat GPT

The emergence of Chat GPT technology represents a significant breakthrough in natural language processing, revolutionizing human-machine interaction and presenting profound challenges to vocational education modernization in China. In the Chat GPT era, vocational education undergoes transformative changes, including personalized learning reaching new heights, big data-driven intelligent decision-making entering a new phase, and the integration of education resources on unprecedented scales. However, this modernization effort also encounters several critical issues, such as overreliance on technology and the associated risks of unemployment, threats to data privacy and security, erosion of ethical values, and the widening digital divide in education. To ensure the high-quality development of vocational education modernization with the support of artificial intelligence tools like Chat GPT, it is crucial to enhance supervision and regulation of Chat GPT technology applications, promoting its safe and responsible use. Moreover, prioritizing technology ethics and moral education becomes imperative to nurture students’ ethical awareness and moral compass in the utilization and development of technology. Lastly, optimizing the distribution of digital educational resources is essential to promote educational equity and bridge the digital divide among students.

EXPLOITING OPTIMAL SELF-ATTENTION DEEP LEARNING-BASED RECOGNITION OF TEXTUAL EMOTIONS FOR DISABLED PERSONS

A disability is a significant issue that has posed and continues to pose a challenge. Disability is a basis of frustration because it can be observed as a mental, constraint, cognitive, and physical handicap that inhibits the individual’s growth and involvement. Consequently, significant effort has been put into removing these kinds of restrictions. These initiatives address the trouble that disabled people encounter. People with disabilities often need to rely on others to meet their requirements. Machine learning (ML) is excelling in producing smart cities and offering a secure environment for disabled individuals. Emotional detection is an important research domain that can expose many appreciated inputs. Emotion is expressed differently through speech and facial expressions, gestures, and written text. Emotion detection in a text document is fundamentally a content-based classification task, utilizing models from deep learning (DL), complex systems and natural language processing (NLP). This paper presents an Optimal Self-Attention DL-based Recognition of Textual Emotions (OSADL-RTE) technique for Disabled Persons. The presented OSADL-RTE technique focuses on identifying distinct types of emotions in the textual data. As a primary preprocessing step, the OSADL-RTE technique comprises different phases to transform the input in a useful way. For word embedding, the bag of words (BoWs) approach is exploited. The OSADL-RTE technique derives self-attention long short-term memory (SA-LSTM) approach to identify emotions. Lastly, the arithmetic fractals optimization algorithm (AOA) approach correctly tunes the hyperparameter selection of the SA-LSTM approach. The experimental study of the OSADL-RTE approach occurs on the emotion database. The investigational outcome of the OSADL-RTE approach portrayed a superior accuracy outcome of 99.59% over existing methods.

Deep Learning-Based Freight Recommendation System for Freight Brokerage Platform

Platform-based businesses in the logistics market are evolving under the influence of digital transformation. Transforming the freight market into an environment where various types of freight can be traded across multiple markets and locations. Freight brokerage platforms have revolutionized the trading relationship between freight owners and vehicle owners. However, this type of system has also introduced inefficiencies, such as unestablished contracts, leading to unnecessary costs and delays. To address this issue, a freight recommendation system can assist users in finding what they are looking for while aiming to reduce failed contracts. With current advances in deep learning, complex patterns based on users’ past behaviors and preferences can be learned, enabling more accurate and personalized recommendations. This study proposes a deep learning-based freight recommendation system to provide personalized services and reduce failed contracts on freight brokerage platforms. The system is built by creating a freight transaction dataset, classifying freight categories through natural language processing and text mining techniques, and incorporating externally derived data on transportation distances. The deep learning model is trained using Autoencoder, Word2Vec, and Graph Neural Networks (GNN), with recommendation logic implemented to suggest suitable freight matches for vehicle owners. This system is expected to increase the market efficiency of the freight logistics industry and is a key step toward improving the long-term profit structure.

Developing a clinical decision support framework for integrating predictive models into routine nursing practices in home health care for patients with heart failure

AbstractBackgroundThe healthcare industry increasingly values high‐quality and personalized care. Patients with heart failure (HF) receiving home health care (HHC) often experience hospitalizations due to worsening symptoms and comorbidities. Therefore, close symptom monitoring and timely intervention based on risk prediction could help HHC clinicians prevent emergency department (ED) visits and hospitalizations. This study aims to (1) describe important variables associated with a higher risk of ED visits and hospitalizations in HF patients receiving HHC; (2) map data requirements of a clinical decision support (CDS) tool to the exchangeable data standard for integrating a CDS tool into the care of patients with HF; (3) outline a pipeline for developing a real‐time artificial intelligence (AI)‐based CDS tool.MethodsWe used patient data from a large HHC organization in the Northeastern US to determine the factors that can predict ED visits and hospitalizations among patients with HF in HHC (9362 patients in 12,223 care episodes). We examined vital signs, HHC visit details (e.g., the purpose of the visit), and clinical note–derived variables. The study identified critical factors that can predict ED visits and hospitalizations and used these findings to suggest a practical CDS tool for nurses. The tool's proposed design includes a system that can analyze data quickly to offer timely advice to healthcare clinicians.ResultsOur research showed that the length of time since a patient was admitted to HHC and how recently they have shown symptoms of HF were significant factors predicting an adverse event. Additionally, we found this information from the last few HHC visits before the occurrence of an ED visit or hospitalization were particularly important in the prediction. One hundred percent of clinical demographic profiles from the Outcome and Assessment Information Set variables were mapped to the exchangeable data standard, while natural language processing–driven variables couldn't be mapped due to their nature, as they are generated from unstructured data. The suggested CDS tool alerts nurses about newly emerging or rising risks, helping them make informed decisions.ConclusionsThis study discusses the creation of a time‐series risk prediction model and its potential CDS applications within HHC, aiming to enhance patient outcomes, streamline resource utilization, and improve the quality of care for individuals with HF.Clinical RelevanceThis study provides a detailed plan for a CDS tool that uses the latest AI technology designed to aid nurses in their day‐to‐day HHC service. Our proposed CDS tool includes an alert system that serves as a guard rail to prevent ED visits and hospitalizations. This tool can potentially improve how nurses make decisions and improve patient outcomes by providing early warnings about ED visits and hospitalizations.

LEVERAGING CORPUS LINGUISTICS AND DATA-DRIVEN DEEP LEARNING FOR TEXTUAL EMOTION ANALYSIS

Emotions have played a major part in the conversation, as they express context to the conversation. Text or words in conversation contain contextual and lexical meanings. In recent times, obtaining emotion from the text has been an attractive area of research. With the emergence of machine learning (ML) algorithms and hardware to aid the ML method, identifying emotion from the text with ML provides significant and promising solutions. The main objective of Textual Emotion Analysis (TEA) is to analyze and extract the user’s emotional states in the text. Many different Complex Systems and Deep Learning (DL) algorithms have been fast-paced developed and proved their effectiveness in several fields including audio, image, and natural language processing (NLP). This has moved researchers away from the classical ML to DL for their academic research work. This study develops a new Corpus Linguistics and Data-Driven Deep Learning for Textual Emotion Analysis (CLD3L-TEA) technique. The CLD3L-TEA technique mainly investigates the distinct types of emotions that endure in the social media text. In the CLD3L-TEA model, the raw data can be pre-processed in distinct ways. Next, a multi-weighted TF–IDF model is used to generate feature vectors. For the identification of emotions, the CLD3L-TEA technique applied a gated recurrent unit (GRU). At last, the hyperparameter range of the GRU model is executed by the Fractal Harris Hawks Optimization (HHO) model. The experimental validation of the CLD3L-TEA technique on a benchmark dataset illustrates the supremacy of this technique over recent approaches.

L1 as a tool for providing corrective feedback

This article delves into the controversial practice of utilizing a student’s first language (L1) as a teaching resource in second language (L2) learning environments. Initially, strategies such as code-switching/code-mixing and translanguaging were considered signs of poor linguistic ability. There was a strong push towards using only the target language in foreign language education, aiming to limit the first language’s interference and foster a deeper immersion in the new language. However, later research has shown the benefits of incorporating the first language in bilingual education and language learning processes. It’s argued that a student’s knowledge in their native language can actually support their comprehension of a second language, suggesting that transferring certain linguistic or conceptual knowledge from L1 to L2 can be advantageous. This perspective encourages the strategic use of this knowledge transfer in teaching methods. Moreover, the text points to positive results from various studies on the positive impact of L1 usage in L2 classrooms. These insights pave the way for further exploration into the application of the first language in adult English as a Second Language (ESL)/English as a Foreign Language (EFL) education, particularly regarding providing corrective feedback.

The sustainable challenge: Where does social psychology stand in achieving the sustainable development goals?

The United Nations Agenda 2030, inclusive of its 17 sustainable development goals (SDGs), serves as the global blueprint for sustainability for both present and future generations. Scientific research is entrusted with the responsibility of contributing by informing the current situation and future challenges in achieving the SDGs. This paper investigates the role of social psychology in contributing to the SDGs and the environmental, economic and social pillars of the UN Agenda. We analysed 4808 papers using Natural Language Processing to identify (i) the relevance of social psychology within the SDG-related literature and (ii) the current and potential contribution of social psychology to the SDGs. Results highlight that social psychology contributes to the SDGs by addressing typical social issues, primarily those related to health and gender, while noting its under-representation in some environmental and economic areas, despite social psychology well-established research on these topics. This paper introduces a novel approach for assessing the SDGs, fostering a critical reflection on the SDG framework and social psychology to guide less explored research paths. This approach could potentially enhance the evaluation and advancement of the 2030 Agenda, facilitating a deeper dialogue between the scientific community and policymakers, driving social change.

Multi-label text classification via secondary use of large clinical real-world data sets.

Procedural coding presents a taxing challenge for clinicians. However, recent advances in natural language processing offer a promising avenue for developing applications that assist clinicians, thereby alleviating their administrative burdens. This study seeks to create an application capable of predicting procedure codes by analysing clinicians' operative notes, aiming to streamline their workflow and enhance efficiency. We downstreamed an existing and a native German medical BERT model in a secondary use scenario, utilizing already coded surgery notes to model the coding procedure as a multi-label classification task. In comparison to the transformer-based architecture, we were levering the non-contextual model fastText, a convolutional neural network, a support vector machine and logistic regression for a comparative analysis of possible coding performance. About 350,000 notes were used for model adaption. By considering the top five suggested procedure codes from medBERT.de, surgeryBERT.at, fastText, a convolutional neural network, a support vector machine and a logistic regression, the mean average precision achieved was 0.880, 0.867, 0.870, 0.851, 0.870and0.805 respectively. Support vector machines performed better for surgery reports with a sequence length greater than 512, achieving a mean average precision of 0.872 in comparison to 0.840 for fastText, 0.837 for medBERT.de and 0.820 for surgeryBERT.at. A prototypical front-end application for coding support was additionally implemented. The problem of predicting procedure codes from a given operative report can be successfully modelled as a multi-label classification task, with a promising performance. Support vector machines as a classical machine learning method outperformed the non-contextual fastText approach. FastText with less demanding hardware resources has reached a similar performance to BERT-based models and has shown to be more suitable for explaining the predictions efficiently.

Understanding public opinion and discussion dynamics of digital humans on social media: an analysis of sentiment, themes and user characteristics

PurposeThe emergence of artificial intelligence-generated content (AIGC) technology has markedly enhanced the capabilities of digital human content generation and natural language processing, thus further advancing the development of digital humans. To enable enterprises and governments to effectively address the challenges and opportunities arising from the rapid development of digital humans, it is imperative to understand the public opinion and discussion dynamics of digital humans.Design/methodology/approachThis study initially analyzed the trends and distribution patterns of public attention to digital humans. By utilizing word cloud technology, we explored the primary focal points of public interest and conducted a topic analysis using latent Dirichlet allocation (LDA) techniques. Subsequently, content analysis was conducted on the popular application domains of digital humans. Finally, this study examined the influence of user characteristics on emotional scores toward digital humans and the presence of differences in focus across user groups.FindingsThe results indicate a sustained increase in public attention toward digital humans, accompanied by notable geographic disparities in the distribution of discussions. Discussions on Weibo are primarily focused on four domains, whereas areas within the digital human application domain that provoke widespread discussion include live streaming, service, cultural entertainment and digital avatars. Significant impacts of user characteristics on sentiment scores were observed, revealing divergent focal points of interest among different user groups toward digital humans.Originality/valueThrough the deep analysis of Weibo data, this study offers new insights into the digital human industry, enabling governments and businesses to understand industry trends and develop targeted digital human customization strategies based on customer characteristics.

The role of biomechanics in enhancing cognitive function and learning outcomes in English language teaching

Language learning, traditionally taught through memory and repetition, often overlooks the role of physical movement in enhancing cognitive and linguistic abilities. Theories like embodied cognition and motor learning suggest that cognitive functions are closely linked to physical actions. Research suggests that posture, gestures, and movement can improve language processing and comprehension. However, the practical integration of biomechanical principles into structured language teaching remains underexplored. Previous studies on gesture-based learning and movement-enhanced vocabulary acquisition indicate potential benefits but lack a comprehensive approach. This study introduces a series of biomechanical interventions in English language teaching, including posture training, movement-based vocabulary learning, sensory-motor integration, and kinesthetic learning techniques. A quasi-experimental design with 115 12–16-year-olds was used for 8 weeks. The experimental group received biomechanical interventions, and the control group received traditional teaching. Cognitive function and language outcomes were assessed pre-and post-assessment. The experimental group showed significant cognitive function improvements, with scores rising from 78.5 ± 5.6 to 89.3 ± 4.8, while the control group showed only a minor change (77.9 ± 5.8 to 80.7 ± 5.5, t = 4.56, p = 0.002, d = 1.78 In the experimental group, vocabulary scores increased from 85.6 ± 7.4 to 92.3 ± 6.2 (t = 3.22, p = 0.008, d = 1.03). Memory improvement correlated strongly with vocabulary acquisition (r = 0.68, p = 0.003). Also, engagement frequency increased (p = 0.004).

Integrating approaches to the role of metaphor in the evolutionary dynamics of language

Abstract Metaphor occupies a central role not only in language use, but also in language change and evolution. Specifically, semantic extension motivated by metaphor plays an important role in extending the lexicon of languages. It is this process that enables the emergence of one of the key properties of modern languages, namely that they are open-ended, systematic, polysemous, structured semiotic systems. Here, we review results from three approaches whose integration presents an important cornerstone for an interdisciplinary account of the role of metaphor in the evolutionary dynamics of language: (1) Historical linguistics and diachronic semantics (2) Computational approaches and natural language processing, and (3) Experimental semiotics. Research in historical linguistics has shown that metaphor is a major mechanism of semantic change. Diachronic semantic analyses have not only mapped detailed historical trajectories of semantic extension motivated by metaphor, but also identified common metaphoric pathways of change as well as shared cognitive principles underlying them. Computational approaches and natural language processing have used findings and data from historical linguistics in attempts to automate the detection of metaphoric semantic change and to build data-driven models models of the principles underlying it. Experimental semiotics is a paradigm in which participants have to create novel communication systems in the absence of language. It represents an experimental design that can investigate cultural linguistic evolution and the emergence of metaphors and metaphorical extensions under controlled laboratory settings to shed light on the interactional and cognitive principles involved in it. Combining results from these approaches represents an important first step towards an interdisciplinary, integrative account of the role of metaphor, and processes of polysemous meaning extension more generally, in the evolutionary dynamics of language.

Vision transformers for glioma classification using T1 magnetic resonance imaging

Automated image analysis and classification have increasingly advanced in recent decades owing to machine learning and computer vision. In particular, deep learning (DL) architectures have become popular in resource-limited and labor-restricted environments such as the health-care sector. Transformer architecture, a DL method with self-attention mechanism, excels in natural language processing; however, its application in image-based diagnosis in health-care sector remains limited. Herein, the feasibility, bottlenecks, and performance of transformers in magnetic resonance imaging (MRI)-based brain tumor classification were investigated. To this end, a vision transformer (ViT) model was trained and tested using the popular Brain Tumor Segmentation (BraTS) 2015 dataset for glioma classification. Owing to limited data availability, domain adaptation techniques were used to pretrain the ViT model and the BraTS 2015 dataset was used for its fine-tuning. With the model only trained for 100 epochs, the confusion matrix for the two-class problem of tumor and nontumor classification showed an overall classification accuracy of 81.8%. In conclusion, although convolutional neural networks are traditionally used for DL-based medical image classification owing to their attention mechanism and long-range dependency-capturing capability, ViTs can outperform them in MRI-based brain tumor classification.

Artificial Intelligence Techniques for Sustainable Reconfigurable Manufacturing Systems: An AI-Powered Decision-Making Application Using Large Language Models

Artificial intelligence (AI) offers a promising avenue for developing sustainable reconfigurable manufacturing systems. Although there has been significant progress in these research areas, there seem to be no studies devoted to exploring and evaluating AI techniques for such systems. To address this gap, the current study aims to present a deliberation on the subject matter, with a particular focus on assessing AI techniques. For this purpose, an AI-enabled methodological approach is developed in Python, integrating fuzzy logic to effectively navigate the uncertainties inherent in evaluating the performance of techniques. The incorporation of sensitivity analysis further enables a thorough evaluation of how input variations impact decision-making outcomes. To conduct the assessment, this study provides an AI-powered decision-making application using large language models in the field of natural language processing, which has emerged as an influential branch of artificial intelligence. The findings reveal that machine learning and big data analytics as well as fuzzy logic and programming stand out as the most promising AI techniques for sustainable reconfigurable manufacturing systems. The application confirms that using fuzzy logic programming in Python as the computational foundation significantly enhances precision, efficiency, and execution time, offering critical insights that enable more timely and informed decision-making in the field. Thus, this study not only addresses a critical gap in the literature but also offers an AI-driven approach to support complex decision-making processes.

A Review of Hybrid Search Architectures: Integrating Keyword and Semantic Search to Optimize Query Relevance

Abstract—The advent of semantic search alongside the tra- ditional robustness of keyword search systems has established an intriguing dichotomy within the landscape of information retrieval. While keyword-based methods excel in terms of speci- ficity, semantic models offer contextual richness and intent aware- ness. However, each paradigm faces challenges in harnessing the strengths of the other. This paper investigates a hybrid search model that integrates both keyword and semantic search techniques, aiming to optimize relevance, interpretability, and user experience in complex information-seeking scenarios. We present an innovative hybrid architecture, conduct a comprehen- sive evaluation of its performance, and discuss its implications for information retrieval in specialized domains such as healthcare and education. Index Terms—Hybrid Search, Keyword Search, Semantic Search, Information Retrieval, Natural Language Processing, Search Architectures

Exploring the Features and Trends of Industrial Product E-Commerce in China Using Text-Mining Approaches

Industrial product e-commerce refers to the specific application of the e-commerce concept in industrial product transactions. It enables industrial enterprises to conduct transactions via Internet platforms and reduce circulation and operating costs. Industrial literature, such as policies, reports, and standards related to industrial product e-commerce, contains much crucial information. Through a systematical analysis of this information, we can explore and comprehend the development characteristics and trends of industrial product e-commerce. To this end, 18 policy documents, 10 industrial reports, and five standards are analyzed by employing text-mining methods. Firstly, natural language processing (NLP) technology is utilized to pre-process the text data related to industrial product commerce. Then, word frequency statistics and TF-IDF keyword extraction are performed, and the word frequency statistics are visually represented. Subsequently, the feature set is obtained by combining these processes with the manual screening method. The original text corpus is used as the training set by employing the skip-gram model in Word2Vec, and the feature words are transformed into word vectors in the multi-dimensional space. The K-means algorithm is used to cluster the feature words into groups. The latent Dirichlet allocation (LDA) method is then utilized to further group and discover the features. The text-mining results provide evidence for the development characteristics and trends of industrial product e-commerce in China.

Assessing Cognitive and Perceptual Abilities in Imaginative and Mainstream Elementary Schools: A Case Comparison

This study investigates the impact of imaginative pedagogy on the cognitive and perceptual abilities of elementary school students aged 7-8, using a case comparison approach. Focusing on a Waldorf school as a case for imaginative education and a mainstream Greek school, the research examines abilities including Spatial, Linguistic, Kinesthetic, Naturalistic, Mathematical and Musical. The study utilized a mixed method consisting of playful assessment activities and classroom observations to evaluate these abilities comprehensively. The findings reveal that students in the Waldorf school demonstrate significant advantages in the Spatial, Linguistic, Kinesthetic and Naturalistic areas, attributed to the school's imaginative teaching methods. More specifically, imaginative pedagogy is shown to resonate well with competences related to physical activities, language processing, understanding of the natural world, and visual-spatial reasoning. In contrast, there are no significant differences in Math-Logic and Musical abilities between the two school types, despite the traditional emphasis on language and mathematics in mainstream Greek schools. The study highlights the potential benefits of imaginative pedagogy for holistic cognitive development, suggesting its applicability in mainstream education.

Development and validation of a novel AI framework using NLP with LLM integration for relevant clinical data extraction through automated chart review.

The accurate extraction of surgical data from electronic health records (EHRs), particularly operative notes through manual chart review (MCR), is complex, crucial, and time-intensive, limited by human error due to fatigue and the level of training. This study aimed to develop and validate a novel Natural Language Processing (NLP) algorithm integrated with a Large Language Model (LLM; GPT4-Turbo) to automate the extraction of spinal surgery data from EHRs. The algorithm employed a two-stage approach. Initially, a rule-based NLP framework reviewed and classified candidate segments from the text, preserving their reference segments. These segments were then verified in the second stage through the LLM. The primary outcomes of this study were the accurate extraction of surgical data, including the type of surgery, levels operated, number of disks removed, and presence of intraoperative incidental durotomies. Secondary objectives explored time efficiency, tokenization lengths, and costs. The performance of the algorithm was assessed across two validation databases, analyzing metrics such as accuracy, sensitivity, discrimination, F1-score, and precision, with 95% confidence intervals calculated using percentile-based bootstrapping. The NLP + LLM algorithm markedly outperformed all performance metrics, demonstrating significant improvements in time and cost efficiency. These results suggest the potential for widespread adoption of this technology.

Natural Language Processing in Robotics: Leveraging Python for Human-Robot Interaction

Natural Language Processing in Robotics: Leveraging Python for Human-Robot Interaction

Enhancing domain-specific text generation for power grid maintenance with P2FT.

The digitization of operation and maintenance in the intelligent power grid equipment relies on a diverse array of information for smart decision-making. In the domain of intelligent decision generation, proficiency is contingent upon extensive learning from copious amounts of text. This necessitates not only robust processing capabilities but also a high level of specialization. In addressing situations where authorization is lacking, pre-trained language models (PLMs) have already provided ideas when confronted with specialized domains or tasks. In consideration of the complexity of textual content in the field of the power grid, which encompasses a multitude of specialized knowledge and involves an abundance of proprietary terminology, we have undertaken an exploration of pre-trained model specialization using the power grid domain as an example, specifically for the task of generating maintenance strategies. A two-stage fine-tuning approach (P2FT) is employed, utilizing a large-scale pre-training model specifically designed for natural language processing. The efficacy and practical value of this method were evaluated through multiple metrics, juxtaposed with other advanced approaches involving low-parameter or parameter-free fine-tuning methods. Through a meticulous analysis and validation of experimental outcomes, we have corroborated the feasibility and practical application value of employing this approach for pre-trained model specialization. Additionally, it has furnished valuable guidance for text generation within both the Chinese language domain and the power grid domain.

Functional connectivity of stimulus-evoked brain responses to natural speech in post-stroke aphasia.

Objective
One out of three stroke-patients develop language processing impairment known as aphasia. The need for ecological validity of the existing diagnostic tools motivates research on biomarkers, such as stimulus-evoked brain responses. With the aim of enhancing the physiological interpretation of the latter, we used EEG to investigate how functional brain network patterns associated with the neural response to natural speech are affected in persons with post-stroke chronic aphasia.

Approach
EEG was recorded from 24 healthy controls and 40 persons with aphasia while they listened to a story. Stimulus-evoked brain responses at all scalp regions were measured as neural envelope tracking in the delta (0.5-4 Hz), theta (4-8 Hz) and low-gamma bands (30-49 Hz) using mutual information. Functional connectivity between neural-tracking signals was measured, and the Network-Based Statistics toolbox was used to: 1) assess the added value of the neural tracking vs EEG time series, 2) test between-group differences and 3) investigate any association with language performance in aphasia. Graph theory was also used to investigate topological alterations in aphasia.

Main results
Functional connectivity was higher when assessed from neural tracking compared to EEG time series. Persons with aphasia showed weaker low-gamma-band left-hemispheric connectivity, and graph theory-based results showed a greater network segregation and higher region-specific node strength. Aphasia also exhibited a correlation between delta-band connectivity within the left pre-frontal region and language performance.

Significance
We demonstrated the added value of combining brain connectomics with neural-tracking measurement when investigating natural speech processing in post-stroke aphasia. The higher sensitivity to language-related brain circuits of this approach favours its use as informative biomarker for the assessment of aphasia.&#xD.