<?xml version="1.0" encoding="utf-8"?>
    <rss version="2.0">
      <channel xmlns:content="http://purl.org/rss/1.0/modules/content/">
        <title>Frontiers in Artificial Intelligence | Natural Language Processing section | New and Recent Articles</title>
        <link>https://www.frontiersin.org/journals/artificial-intelligence/sections/natural-language-processing</link>
        <description>RSS Feed for Natural Language Processing section in the Frontiers in Artificial Intelligence journal | New and Recent Articles</description>
        <language>en-us</language>
        <generator>Frontiers Feed Generator,version:1</generator>
        <pubDate>2026-07-08T18:16:23.235+00:00</pubDate>
        <ttl>60</ttl>
        <item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frai.2026.1842542</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frai.2026.1842542</link>
        <title><![CDATA[PaSTO-GNN: prompt-aware spatio-temporal graph neural networks for automatic essay scoring]]></title>
        <pubdate>2026-07-08T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Areej Alhothali</author>
        <description><![CDATA[Automatic Essay Scoring (AES) aims to evaluate the quality of written essays automatically, providing fast, consistent, and objective assessments of students' writing ability. Existing deep learning approaches—including recurrent, convolutional, and transformer-based models—primarily focus on textual semantics, yet they often overlook the spatio-temporal nature of essay composition, where meaning evolves across sentences and paragraphs through discourse progression. To address this gap, this study presents a prompt-aware Spatio-Temporal Graph Neural Network (PaSTO-GNN) for AES. In this framework, each essay is first segmented into sentences, and each sentence is represented as a node in a spatio-temporal graph. The feature representation of each node is constructed by combining contextual sentence embeddings extracted from a RoBERTa encoder adapted via Low-Rank Adaptation (LoRA), semantic embeddings obtained from Sentence-BERT (SBERT), and a learned prompt embedding that conditions scoring on the essay prompt. Spatial edges capture semantic relationships between sentences, while temporal edges encode the sequential progression of ideas throughout the essay. The resulting node representations are processed through a spatio-temporal message passing network, followed by a BiGRU layer and temporal attention pooling to obtain a global essay representation. To model the ordered nature of essay scores, prompt-specific ordinal prediction heads based on CORAL are employed, together with a per-prompt calibration step that better aligns predicted scores with human scoring distributions. Experimental results on the AES 2.0 benchmark dataset show that PaSTO-GNN achieves a Quadratic Weighted Kappa (QWK) of 0.8329 on the validation set after prompt calibration and a Pearson correlation of 0.8168, highlighting the effectiveness of combining spatio-temporal discourse modeling with prompt-aware representations for automated essay evaluation.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frai.2026.1810552</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frai.2026.1810552</link>
        <title><![CDATA[Sentiment trajectory modeling in mental health discussions via transformer-based analysis]]></title>
        <pubdate>2026-06-26T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Shaina U</author><author>Tamilarasi Kathirvel Murugan</author><author>Akshaya Poorna R</author><author>Logeswari Govindaraj</author><author>Joel Prince</author>
        <description><![CDATA[IntroductionSocial media platforms produce a constant stream of user-generated text, which holds promise as a source of useful information regarding population-level and individual mental health states. However, existing methods for sentiment analysis on social media texts process these texts as individual, static instances, failing to consider the underlying dynamics of emotional states, which are naturally evolving in nature.MethodsIn this paper, we introduce a novel framework for Temporal Sentiment Progression Analysis, which uses domain-specific transformer architectures to reconstruct the entire emotional evolution of a user over a given period of time. This framework captures important aspects of emotional evolution, including variability, volatility, and key emotional inflection points in a user’s emotional progression. Our approach performs per-comment sentiment and thematic classification using transformer models, followed by post hoc statistical analysis to examine temporal patterns in user discussions. The framework was evaluated on a dataset of approximately 4,000 Reddit comments collected from eight mental health-related subreddits, with additional synthetic samples used only during training to address class imbalance. Performance evaluation was conducted exclusively on authentic Reddit comments and further validated using external Reddit-based mental health datasets and comparative baseline experiments.ResultsOur experimental results on a large-scale social media dataset reveal different sentiment progression archetypes, which are strongly correlated with self-reported mental health concerns. Our proposed framework achieves a high classification accuracy of 92%.DiscussionOur approach shifts from static sentiment analysis to dynamic sentiment progression analysis, which can help in understanding the evolution of emotional distress in a more nuanced manner and can be useful in the development of context-aware interventions for mitigating mental health concerns.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frai.2026.1813668</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frai.2026.1813668</link>
        <title><![CDATA[Automatic speech recognition for Telugu: a comparative analysis of Wav2Vec 2.0 model variants and hyperparameter tuning]]></title>
        <pubdate>2026-06-24T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Anvita Manne</author><author>Nikhita James</author><author>Ishaan Jain</author><author>Jagalingam Pushparaj</author>
        <description><![CDATA[Automatic speech recognition (ASR) systems for the Telugu language can significantly enhance speech-based interaction and digital accessibility. Although Telugu is among the top languages in the Dravidian family in terms of the number of speakers, there is a notable absence of standardized, noise-robust resources for Telugu ASR development. The existing systems are not specifically designed for low-resource languages like Telugu. The datasets available for Telugu are restricted to controlled environments and do not include noise interferences. Most existing ASR models are optimized for high-resource languages such as English, leading to poor generalization when applied to Telugu. In this work, we fine-tuned three pre-trained variants of the Wav2Vec 2.0 model: XLS-R 300M, XLSR-53, and XLS-R-1B on a curated 48.4-h Telugu speech corpus compiled from Mozilla Common Voice, OpenSLR, and the Hugging Face dataset. This study utilized a comprehensive ablation study on four hyperparameters to evaluate their recognition performance using word error rate (WER) and character error rate (CER) as evaluation metrics. Among the three models, the fine-tuned XLS-R-1B performed the best, achieving a WER of 36.23% and CER of 15.44%, followed by the XLSR-53 (WER 37.78%, CER 15.81%) and XLS-R-300M (WER 37.87%, CER 15.81%). An important consideration of the research was finding the right trade-off between a model's complexity and the accuracy of its performance. The results showed that the size of the model is important, and hyperparameter selection significantly influences the development of an effective ASR model in low-resource languages such as Telugu.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frai.2026.1794334</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frai.2026.1794334</link>
        <title><![CDATA[Identification of key sentences in a text]]></title>
        <pubdate>2026-06-23T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>N. Veer Viswajit</author><author>L. Jeganathan</author><author>M. Janaki Meena</author><author>Jayaram Balabaskaran</author><author>Ummity Srinivasa Rao</author>
        <description><![CDATA[Human evaluation of students' summative assessments requires significant time and cognitive effort. Existing automated evaluation models struggle to provide accurate evaluations for various reasons. The complexity increases with lengthy student responses, which may contain both relevant and irrelevant information. Therefore, there is a need for an intermediate mechanism to assist human evaluation. To address this gap, this study presents, as a proof of concept, a semantic-aware model called the Key Sentence Identifier (KSI), which extracts sentences that are relevant to the topic and coherent with the meaning conveyed in the student's response. Unlike traditional approaches that rely primarily on keyword matching, KSI employs a dual-embedding framework that integrates transformer-based contextual embeddings (BERT) with semantic similarity-based sentence representations (SBERT). Furthermore, a dedicated dataset has been curated for this task to enable effective training and evaluation of the model. Ablation analysis indicates that the SFT BERT + SFT SBERT configuration achieves the best performance, improving the F1 score from 50.32% (BERT + SBERT) to 86.01%. Furthermore, comparisons with LoRA-based fine-tuning and standard baseline methods show that the proposed KSI model consistently outperforms alternative approaches in identifying contextually relevant sentences. The novelty of the proposed KSI lies in its ability to efficiently evaluate descriptive answers by identifying contextually relevant content for human evaluation. In addition to reducing evaluation time, KSI minimizes the cognitive effort required by human evaluators by eliminating the need to manually identify irrelevant content. Thus, the use of KSI has the potential to enhance the quality of evaluation by enabling evaluators to focus on contextually relevant content while reducing cognitive effort. As improvements in evaluation quality can positively influence learning outcomes, KSI can, more broadly, contribute to the realization of United Nations Sustainable Development Goal 4. A distinguishing feature of the KSI model is that it assists human evaluation and supports integration with future automated evaluation systems.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frai.2026.1771115</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frai.2026.1771115</link>
        <title><![CDATA[Detecting “large language models fingerprint” for Japanese texts generated by six LLMs]]></title>
        <pubdate>2026-06-22T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Wataru Zaitsu</author><author>Mingzhe Jin</author><author>Shunichi Ishihara</author><author>Satoru Tsuge</author><author>Mitsuyuki Inaba</author>
        <description><![CDATA[BackgroundLarge language models (LLMs) have developed rapidly since the release of ChatGPT by OpenAI on November 30, 2022. This vogue comes to mind us “Can we distinguish LLMs-generated texts each other?” “What stylometric features are effective for differentiating LLMs such as fingerprints?” The purpose of this study was to distinguish the 300 Japanese texts (e.g., public comments) generated by six LLMs [ChatGPT (GPT5), Claude 3.5, Gemini, Microsoft Copilot, Llama 3.1, and Perplexity].MethodsTo this end, we explored the effective stylometric features [e.g., function-word unigrams, part of speech (POS) bigrams, and phrase patterns] using the following analyses: (1) UMAP (Uniform Manifold Approximation and Projection) to visually explore distributional differences among LLMs, (2) Random Forest (RF) and XGBoost with leave-one-out cross-validation to differentiate LLMs, and (3) SHAP (SHapley Additive exPlanations) based on Random Forest to identify effective stylometric features.ResultsFirst, UMAP demonstrated the separation of texts among the five LLMs except for Llama 3.1, which displayed substantial overlap with the other five LLMs. Second, RF achieved the highest performance across all stylometric features, with macro F1 scores exceeding 0.95 and reaching 1.00 for several LLMs. The detection performance of XGBoost was lower than that of RF, with the macro F1 scores ranging from 0.88 to 0.94. Finally, SHAP revealed LLM-specific patterns in function-word unigrams, POS bigrams, and phrase patterns.ConclusionThese findings indicate that Japanese public comments generated by the six LLMs can be accurately distinguished by focusing on the combination or patterns of stylometric features, suggesting LLM-specific linguistic fingerprints regardless of similarities in the underlying transformer architectures.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frai.2026.1800372</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frai.2026.1800372</link>
        <title><![CDATA[Logic, inference, understanding: cross-domain generalization for generative language models]]></title>
        <pubdate>2026-06-16T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Rasmus Blanck</author><author>Bill Noble</author>
        <description><![CDATA[Neural systems for Natural Language Inference (NLI) have seen impressive performance over the last ten years, but their ability to generalize beyond their training data has repeatedly been questioned. The NLI task has long been considered as a proxy for the wider problem of Natural Language Understanding (NLU), implicitly motivated by relying on an inferentialist conception of semantics. This paper draws on insights from work in formal logic and semantics to introduce distinctions between different notions of generalizations (in-domain vs. cross-domain, and linguistic vs. inferential) in an attempt to disentangle the problem of generalization. We leverage the theoretical contributions in experiments addressing the inferential generalization power of autoregressive NLI models.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frai.2026.1732901</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frai.2026.1732901</link>
        <title><![CDATA[The quantified immune-aging dysregulation index: a large-language model-powered method for annotating and quantifying systems-level dysregulation]]></title>
        <pubdate>2026-06-08T00:00:00Z</pubdate>
        <category>Methods</category>
        <author>George D. Vavougios</author><author>Georgios Hadjigeorgiou</author>
        <description><![CDATA[BackgroundPathway enrichment analyses are widely used to interpret transcriptomic datasets; however, their outputs typically consist of lists of statistically enriched pathways that require qualitative interpretation and are difficult to compare across biological contexts. Methods of semantic classification that transform enrichment results into quantitative, mechanistically interpretable measures of system-level dysregulation remain underexplored.MethodsHere, we introduced TENSE (quanTifiEd immuNe-aging dySregulation index), a framework that summarizes pathway enrichment outputs into a quantitative estimate of immune-aging–associated dysregulation. Utilizing a Large Language Model classifier via a KNIME workflow, significantly enriched pathways are semantically classified into five mechanistic categories representing key processes implicated in immune aging, the DIRES scheme: DNA damage (D), DNA repair (R), epigenetic drift (E), inflammaging (I), and nucleic acid sensing (S). These pathway-derived signals are then aggregated into a normalized dysregulation score reflecting the magnitude (TENSE) and distribution (DIRES) of aging-associated processes across biological contexts.ResultsApplication of TENSE to transcriptional modules derived from neurodegenerative, radiation-response, and immune activation datasets revealed distinct dysregulation profiles. Alzheimer’s disease–associated modules were primarily characterized by inflammaging signatures, particularly within microglial transcriptional programs, whereas radiation response datasets exhibited dominant DNA damage-related signals. Sepsis-associated gene signatures showed strong inflammatory contributions, producing the highest TENSE values observed. Robustness analysis demonstrated high reproducibility of pathway classification across repeated runs and close agreement between large language model–derived annotations and human consensus scores.ConclusionTENSE provides a reproducible and interpretable method for transforming pathway enrichment outputs into quantitative estimates of system-level immune-aging dysregulation. By bridging pathway enrichment analysis and mechanistic interpretation, the framework enables comparative analysis of aging-related biological processes across diverse datasets.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frai.2026.1804284</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frai.2026.1804284</link>
        <title><![CDATA[Can small language models handle context-summarized multi-turn customer-service QA? A synthetic data-driven comparative evaluation]]></title>
        <pubdate>2026-06-02T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Lakshan Cooray</author><author>Deshan Sumanathilaka</author><author>Pattigadapa Venkatesh Raju</author>
        <description><![CDATA[Customer-service question answering (QA) systems increasingly rely on conversational language understanding. While Large Language Models (LLMs) achieve strong performance, their high computational cost and deployment constraints limit practical use in resource-constrained environments. Small Language Models (SLMs) provide a more efficient alternative, yet their effectiveness for multi-turn customer-service QA remains underexplored, particularly in scenarios requiring dialogue continuity and contextual understanding. In this study, we evaluate whether instruction-tuned SLMs, fine-tuned using parameter-efficient finetuning, can effectively handle context-summarized multi-turn customer-service QA while preserving contextual consistency, response quality and task relevance under computational constraints. We further investigate instruction-tuned SLMs for context-summarized multi-turn customer-service QA using a history summarization strategy to preserve essential conversational state and introduce a conversation stage-based qualitative analysis to evaluate model behavior across different phases of customer-service interactions. The main contributions of this work include the application of parameter-efficient fine-tuning to adapt SLMs for context-summarized multi-turn customer-service QA, a synthetic data construction pipeline for generating a context-summarized multi-turn QA dataset, and a structured evaluation framework combining quantitative metrics with human and LLM-as-a-judge assessments for customer-service QA evaluation. Nine instruction-tuned SLMs are evaluated against three commercial LLMs using lexical and semantic similarity metrics alongside qualitative assessments, including human evaluation and LLM-as-a-judge methods. Results show notable variation across SLMs, with some models demonstrating near-LLM performance, while others struggle to maintain dialogue continuity and contextual alignment. These findings highlight both the potential and current limitations of low-parameter language models for real-world customer-service QA systems.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frai.2026.1743223</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frai.2026.1743223</link>
        <title><![CDATA[Critical analysis of datasets for sign language translation]]></title>
        <pubdate>2026-05-29T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Bittor Alkain</author><author>Adrián Núñez-Marcos</author><author>Carlos Escolano</author><author>Laura Docío-Fernández</author><author>Olatz Perez-de-Viñaspre</author><author>Gorka Labaka</author>
        <description><![CDATA[IntroductionIn recent years, significant progress has been made in Machine Translation (MT), including multilingual and low-resource settings. However, Sign Language Translation (SLT) remains underdeveloped, largely due to the scarcity of high-quality datasets and the overreliance on a few small, widely used benchmarks. This study aims to critically assess the datasets most commonly used in SLT research to determine whether their characteristics may lead to overfitting and misleading evaluation results.MethodsWe then conduct a detailed empirical study comparing training and test set similarity for PHOENIX14T, CSL-Daily, and LSE-Health. Using both gloss-based (TwoStream-SLT) and gloss-free (GFSLT-VLP) models, we evaluate the extent to which models memorize training data and how this affects BLEU scores.ResultsOur analysis reveals that PHOENIX14T exhibits substantial overlap between training and test sets, leading to inflated BLEU scores and can even mask signs of overfitting. CSL-Daily shows less overlap and more robust generalization. We also show that a small subset of “training-like” sentences disproportionately contributes to BLEU scores.DiscussionWe recommend that future SLT research move away from overused benchmarks and adopt larger, more diverse datasets such as How2Sign, CSL-News, and FLEURS-ASL. We also advocate for a shift toward gloss-free approaches and more careful interpretation of evaluation metrics, especially in low-resource settings.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frai.2026.1797587</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frai.2026.1797587</link>
        <title><![CDATA[Neuro-symbolic NLP: taxonomy, assessment, and directions]]></title>
        <pubdate>2026-05-22T00:00:00Z</pubdate>
        <category>Systematic Review</category>
        <author>Stergios Chatzikyriakidis</author><author>Shalom Lappin</author>
        <description><![CDATA[Neuro-symbolic (NeSy) approaches promise to overcome the limitations of purely neural and purely symbolic NLP. In this paper we survey recent development in NeSy NLP and propose a system classification framework that combines Kautz's integration types with Lappin's injective-federative distinction. We then apply this taxonomy and show that even though federative architectures consistently outperform injective approaches, they remain underexplored. We assess performance across compositional generalization, reasoning, and robustness benchmarks, examine existing attempts at linguistic theory integration, and show how the taxonomy can guide future architectures that preserve the strengths of formal linguistic frameworks. We conclude with directions for scaling federative systems and exploring tighter integration.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frai.2026.1791624</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frai.2026.1791624</link>
        <title><![CDATA[An LLM-based methodology for the automatic detection of bias in the DuoWikiBias corpus]]></title>
        <pubdate>2026-05-13T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Karla Salas-Jimenez</author><author>Sergio-Luis Ojeda-Trueba</author><author>Gemma Bel-Enguix</author><author>Edgar Lee-Romero</author><author>Francisco F. López-Ponce</author>
        <description><![CDATA[Bias detection remains a challenge in Natural Language Processing, particularly in non-English contexts, due to the conceptual ambiguity of bias and the scarcity of annotated resources. This study addresses the lack of Spanish-language resources by investigating the automatic detection of framing, epistemological, and demographic1 biases. We introduce DuoWikiBias, a novel parallel corpus derived from Wikipedia for Spanish bias classification. We evaluate Large Language Models (Llama and Gemma) using advanced prompting techniques—CARP and Metacognition—combined with a Gradient Ascent unlearning method to refine model attention. Their performance is compared against classical approaches, including logistic regression with S-BERT embeddings and linguistic features. Results show that advanced prompting substantially improves performance over simple instructions, while the best overall performance (F1 = 0.796) is achieved by combining CARP-based features with Gradient Ascent and a Support Vector Machine classifier. These findings suggest that LLMs are effective for bias-aware representation learning, but hybrid approaches with traditional classifiers remain competitive. This work provides both a validated dataset and a methodological framework for bias detection in Spanish NLP.2]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frai.2026.1783410</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frai.2026.1783410</link>
        <title><![CDATA[How to systematically and quantifiably remove meaning?]]></title>
        <pubdate>2026-05-13T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Frida Proschinger Åström</author><author>Arend Hintze</author>
        <description><![CDATA[Large language models increasingly mediate real-world tasks, yet we lack systematic ways to quantify how their performance degrades when the meaning of their inputs is eroded. To bridge this gap, we developed a framework to semantically erode meaning and quantify its intensity, grounded in discourse analysis, psycholinguistics, and software engineering, comprising five theoretically motivated methods: omission of key information and context, lexical substitution with near-synonyms, increased abstraction, structural obfuscation and renaming, and injection of logical errors. We applied these erosion operators across five domains and quantified their effects on model performance using a publicly available language model. A two-way Analysis of Variance (ANOVA) revealed significant main effects of both domain and erosion method, as well as a significant interaction, indicating that the impact of semantic degradation depends jointly on how text is eroded and how domain-specific information is encoded. Logical error erosions proved especially damaging for code generation, whereas structural obfuscation most strongly impaired news and instruction tasks. Epistasis analysis of pairwise erosion unions showed that some combinations produced super-additive degradation while others exhibited compensatory effects. These domain-by-erosion profiles provide diagnostic insight into where multi-step large language model (LLM) pipelines are most likely to fail and suggest that robustness benchmarks should probe models along domain-specific vulnerability dimensions rather than relying on generic perturbations. Semantic erosion thus offers a principled tool for turning model failure into evidence about how language models structure and degrade meaning.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frai.2026.1801094</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frai.2026.1801094</link>
        <title><![CDATA[Putting reasons back into reasoning: how genuine reasoning is inference-based and why neuro-symbolic NLI could achieve it]]></title>
        <pubdate>2026-05-08T00:00:00Z</pubdate>
        <category>Conceptual Analysis</category>
        <author>Reto Gubelmann</author>
        <description><![CDATA[Taking Leibniz' ideal of a universal truth-calculating machine as a vantage point, this article provides a philosophically sound analysis of the concept of reasoning in NLP. It argues that reasoning always involves inference, which in turn requires being guided by reason relations. Based on this, the article argues that Symbolic NLP is unable to reason for epistemic reasons on the part of humans, that Neural NLP is likely unable to do so in principle, and that Neuro-Symbolic NLP is ideally set up to succeed where the two approaches in isolation failed, that is, to progress toward realizing Leibniz' vision of a truth-calculating machine—to the extent to which this is possible. We conclude by providing a theoretical grounding for the latter claim in the philosophy of a contemporary rationalist, namely Robert Brandom.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frai.2026.1790240</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frai.2026.1790240</link>
        <title><![CDATA[Analyzing gendered patterns in sentiments in comments under STEM YouTube channels]]></title>
        <pubdate>2026-05-07T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Isha Karn</author><author>N. Ilakiyaselvan</author><author>V. Kalyanasundaram</author><author>A. J. Keerthi</author>
        <description><![CDATA[This study explores gender-based disparities in sentiments and discourse in comments on YouTube channels focused on science, technology, engineering, and mathematics (STEM). Although there have been considerable efforts toward achieving gender equality, biases and inequalities still persist in the representation of women in STEM, including in online communication spaces. A mixed-methods approach was used, based on a dataset of comments collected from over 100 curated STEM YouTube channels using the YouTube Data API. The comments were cleaned and preprocessed, and information regarding the gender of content creators was identified. The RoBERTa model was used for sentiment annotation, while Latent Dirichlet Allocation (LDA) was applied for topic modelling. The results show a clear difference in sentiment distribution between genders. Negative comments were more frequent in female-hosted channels and were often related to personal criticism, objectification, and appearance-based remarks, whereas comments on male-hosted channels were more neutral and focused on content. The findings indicate the presence of gender bias in online discussions related to STEM.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frai.2026.1815243</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frai.2026.1815243</link>
        <title><![CDATA[When news is “written by artificial intelligence”: a systematic review of provenance and disclosure cues in journalism and their effects on credibility and trust]]></title>
        <pubdate>2026-05-05T00:00:00Z</pubdate>
        <category>Systematic Review</category>
        <author>Lorena Licenji</author><author>Julian Hoxha</author>
        <description><![CDATA[IntroductionArtificial intelligence (AI) is increasingly embedded in journalism, yet audience responses may depend on both AI provenance, meaning who or what is presented as having written the story, and transparency cues that disclose AI use. This systematic literature review synthesises empirical studies examining how AI provenance cues and AI disclosure cues in journalism affect perceived credibility and trust.MethodsFollowing PRISMA 2020 and PRISMA-S, Scopus and Web of Science Core Collection were searched on 2 February 2026 for English-language, peer-reviewed journal articles and conference papers. Searches yielded 492 records. After deduplication and pre-screen exclusions, 290 records were screened at title/abstract level, and 47 studies with retrievable full texts were included. A structured narrative synthesis was conducted, guided by the Synthesis Without Meta-analysis (SWiM) guideline, to map study designs, cue operationalisations, outcome targets (message, source, outlet), and moderators.ResultsAcross heterogeneous designs, AI provenance cues were not associated with a consistent “AI penalty”: most extractable results indicated no difference between AI-attributed and human-attributed news, and observed effects were typically conditional on topic, baseline trust, outlet/source cues, and whether human oversight was signalled. Evidence on disclosure cues was limited (10 studies) and was dominated by null or conditional findings. Scepticism appeared more likely when disclosures implied full automation without accompanying accountability or oversight information.DiscussionA Cue–Inference–Target (CIT) framework is proposed to explain when AI cues shift epistemic-quality versus normative-legitimacy judgments. Future research should use factorial designs that separate provenance from disclosure and standardise reporting of cue wording, placement, and validated outcome measures.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frai.2026.1724407</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frai.2026.1724407</link>
        <title><![CDATA[SSABE-TSCM: drift-aware and interpretable financial sentiment analysis for low-resource Bangla via adaptive semi-supervised and temporal contrastive modeling]]></title>
        <pubdate>2026-04-08T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Iftakhar Ali Khandokar</author><author>Priya Deshpande</author>
        <description><![CDATA[Analyzing the tone of Bangla financial news is challenging because labeled data are scarce, the language is morphologically rich, and economic discourse shifts over time. We address these hurdles with a three-part framework. First, SSABE a Semi-Supervised Adaptive Boosting Ensemble iteratively refines pseudo-labels, adjusts model weights by recent performance, and applies sector-aware voting to distill reliable labels from limited data. Second, the Temporal Sentiment Contrastive Module (TSCM) aligns yearly embedding prototypes via contrastive loss, keeping the classifier robust against vocabulary drift and shifting economic regimes. Third, Temporal-SHAP yields token-level attributions that reveal how term importance changes across years and industries, thereby making the system transparent to analysts. Evaluated on a 5-year (2018–2023) Bangla financial news corpus spanning eight sectors, our pipeline attains a macro-F1 of 0.782 and 91.4 % explanation fidelity surpassing fine-tuned transformer and self-training baselines by 6 %–12 % absolute. Performance remains stable when labels are scarce, sectors are imbalanced, or economic shocks such as the inflation and currency decline of 2023 occur. Moreover, yearly sentiment scores and Temporal-SHAP attributions track inflation and exchange-rate trends, confirming real-world relevance. The proposed framework offers a scalable, interpretable solution for monitoring emerging-market news, supporting regulators, policymakers, and investors who rely on trustworthy Bangla-language insights.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frai.2026.1781552</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frai.2026.1781552</link>
        <title><![CDATA[On the interface between linguistics, computer science and psychiatry: analyzing textual key-factors affecting BERT-based classification of schizophrenia in social media texts]]></title>
        <pubdate>2026-04-08T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>J. V. Miranda e Silva</author><author>C. Rodrigues</author><author>E. Vital Brazil</author>
        <description><![CDATA[IntroductionThis paper investigates language impairments in schizophrenia (SZ) by analyzing the decision-making process of a transformer-based model in discriminating between texts produced by persons with SZ and persons without SZ. By doing so, we integrate insights from language-centered investigations with computational approaches. Using BERT-base-cased, we explore how linguistic markers of SZ can be identified through Natural Language Processing (NLP) techniques, with emphasis on improving performance reliability via dataset refinement and approaching interpretability of deep learning outputs via statistical analyses of thematic content.MethodsWe report the fine-tuning of a BERT model for text classification of 31,278 Reddit posts (15,639 SZ, 15,639 controls). The experiment evaluated the capacity of the model to distinguish language produced by individuals with SZ.ResultsThe model achieved moderate performance (Accuracy = 0.6969; AUC = 0.78) and remained stable across hyperparameter configurations, indicating that foundation models such as BERT fit to data and, therefore, further performance gains are more likely to be derived from dataset refinement than from additional hyperparameter optimization. There were three key factors affecting the model’s performance: text length, topic of discussion and vocabulary choices. Posts that were correctly classified tended to be significantly longer (p < 0.001, M = 37.30), focused on certain specific topics (e.g., r/Christianity), and contained more words related to mental health conditions, particularly those semantically related to SZ.DiscussionThese factors have also been reported in manual analyses of the impacts of SZ on language. These findings contribute to the accuracy of computational models aimed at working on linguistic classification tasks and underscore the value of carefully curated datasets, while demonstrating the viability of NLP methods in profiling SZ language.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frai.2026.1766899</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frai.2026.1766899</link>
        <title><![CDATA[A multi-layer annotated corpus for information extraction in Russian clinical NLP]]></title>
        <pubdate>2026-03-31T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Anar Sultangaziyeva</author><author>Madina Sambetbayeva</author><author>Nurzhan Mukazhanov</author><author>Bayangali Abdygalym</author><author>Sandugash Serikbayeva</author>
        <description><![CDATA[IntroductionClinical exome sequencing reports contain valuable genetic and phenotypic information but are typically stored in unstructured text form, making automated biomedical information extraction challenging. For the Russian language, publicly available annotated corpora for genetic report analysis remain extremely limited.MethodsWe present GENEXOM, the first multi-level annotated corpus of Russian-language clinical exome sequencing reports designed for biomedical information extraction. The corpus includes 5,318 reports (318 authentic and 5,000 synthetic) and comprises 16 entity types and 7 relation types aligned with HGVS, OMIM, ClinVar, and ACMG/AMP standards. Annotation was performed in the Label Studio platform by expert geneticists. Baseline transformer models (RuBERT, RuBioBERT, ModernBERT) were fine-tuned for Named Entity Recognition (NER) and Relation Extraction (RE).ResultsThe annotation achieved span-level F1-IAA = 0.83 and macro κ = 0.79 ± 0.04, indicating substantial inter-annotator agreement. Among the evaluated models, ModernBERT achieved the best performance with F1 = 0.88 ± 0.03 for NER and F1 = 0.836 ± 0.04 for RE on the held-out test set.DiscussionThe GENEXOM corpus provides a linguistically and clinically adapted resource for Russian medical NLP and supports downstream tasks such as variant interpretation, phenotype–disease mapping, and biomedical knowledge graph construction. The corpus and accompanying code are publicly available for research purposes.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frai.2026.1749517</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frai.2026.1749517</link>
        <title><![CDATA[From simulated empathy to structural attunement: Realtime Editable Memory Topology and the evolution of emotionally grounded AI]]></title>
        <pubdate>2026-03-31T00:00:00Z</pubdate>
        <category>Perspective</category>
        <author>John Albanese</author>
        <description><![CDATA[Large language models (LLMs) and retrieval-augmented generation (RAG) systems have achieved remarkable linguistic fluency, and many now implement persistent cross-session memory at the application layer. However, these mechanisms typically rely on external storage and reinjection of stored content rather than structural reorganization of memory relationships. As a result, they remain limited in their ability to integrate affective salience into a dynamically evolving internal memory topology capable of supporting coherent long-term behavior. To address this gap, we introduce Realtime Editable Memory Topology (REMT), an architectural framework for imbuing conversational agents with persistent autobiographical memory organized as an evolving graph of emotionally valenced nodes. REMT formalizes synthetic neuroplasticity through explicit update rules governing edge reinforcement, decay, and pruning, and introduces a bounded Mood Index that modulates retrieval bias and response generation as a function of accumulated affective experience. In this Perspective, we argue that memory-grounded architectures integrating insights from cognitive science, affective computing, and memory-augmented neural systems are necessary for building adaptive conversational agents with stable long-term interactional tendencies. We conclude by outlining a roadmap for empirical validation using an internally developed evaluation framework, with results to be reported in a future Original Research article.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/frai.2026.1768701</guid>
        <link>https://www.frontiersin.org/articles/10.3389/frai.2026.1768701</link>
        <title><![CDATA[Advanced feature selection and temporal attention mechanisms with Bi-LSTM classifier for optimizing emotion recognition in Kashmiri speech]]></title>
        <pubdate>2026-03-18T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>GH Mohmad Dar</author><author>Radhakrishnan Delhibabu</author>
        <description><![CDATA[This study introduces an advanced methodology for enhancing emotion recognition in Kashmiri speech by leveraging optimized feature selection and integrating temporal attention mechanisms into Long Short-Term Memory (LSTM) networks. A meticulous feature selection process identified key acoustic features, including Mel Frequency Cepstral Coefficients (MFCCs), Linear Predictive Coding (LPC), and other relevant descriptors, as optimal for emotion classification. The incorporation of temporal attention layers significantly improved the model's capacity to capture complex emotional patterns and temporal dynamics within the speech data. The proposed attention-augmented LSTM model achieved an accuracy of 90.2%, outperforming the baseline LSTM model's accuracy of 86%. Notable improvements in precision, recall, and F1-scores across multiple emotional categories further highlight the efficacy of the attention mechanism in capturing subtle emotional variations. In addition to performance gains, the study provides a clear research direction by demonstrating how attention–based temporal modeling can benefit low-resource languages such as Kashmiri, where linguistic and prosodic cues differ significantly from widely studied languages. The findings therefore establish a methodological baseline that supports future SER deployments in digital domains, including chat-based systems, affect-aware agents, and other human–machine interfaces. These findings underscore the model's ability to enhance both the sensitivity and specificity of emotion recognition systems, offering a robust and efficient framework for speech-based emotion analysis. Future work will extend the proposed methodology to multilingual settings and incorporate multimodal information, enabling deeper analysis of emotional expression across diverse linguistic and cultural contexts.]]></description>
      </item>
      </channel>
    </rss>