Natural Language Processing Journal最新文献

{"title":"BNDrop-XNet: A lightweight multi-modal deep learning framework with weighted fusion for melanoma detection using dermoscopic and clinical data","authors":"Samad Najjar-Ghabel,&nbsp;Shamim Yousefi","doi":"10.1016/j.nlp.2026.100232","DOIUrl":"10.1016/j.nlp.2026.100232","url":null,"abstract":"<div><div>Melanoma is one of the most life-threatening forms of skin cancer, where early detection improves survival outcomes. Despite recent advances in Deep Learning (DL) for dermoscopic image analysis, existing approaches face critical limitations, including reliance on image-only inputs, inability to incorporate clinical metadata, and unstable performance on imbalanced datasets observed in real-world screening. Furthermore, many architectures are computationally heavy, limiting their suitability for clinical deployment. To address these gaps, in this paper, we propose a lightweight multi-modal diagnostic framework (BNDrop-Xnet) that integrates dermoscopic image features with clinical metadata (age, sex, and lesion location) through a weighted fusion strategy. Our framework consists of four main stages: (1) data preprocessing, including normalization, stratified sampling, and SMOTE-based class balancing; (2) image model creation using a four-block Convolutional Neural Network (CNN) architecture with Conv2D, Batch Normalization, MaxPooling, and Dropout layers; (3) metadata model construction with impact-factor computation and Multi-Layer Perceptron (MLP)-based learning of diagnostic feature contributions; and (4) ensemble integration through a weighted fusion and decision threshold optimization. It was evaluated on the ISIC 2020 dataset and achieved 90% accuracy, 80% precision, and a 73% F1-Score, outperforming several state-of-the-art models. These results demonstrate the effectiveness of combining visual and clinical cues for robust melanoma screening. BNDrop-XNet offers an efficient, interpretable, and meaningful solution for real-world deployment in resource-limited environments.</div></div>","PeriodicalId":100944,"journal":{"name":"Natural Language Processing Journal","volume":"16 ","pages":"Article 100232"},"PeriodicalIF":0.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148949783","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hallucination detection, verification, and correction in generative AI: A comprehensive survey","authors":"Syed Muhammad Waqas,&nbsp;Ziyan Umer,&nbsp;Affan Alim,&nbsp;Muhammad Arham,&nbsp;Mazliham Mohd Su’ud,&nbsp;Syed Jafar Ali Kazmi,&nbsp;Muhammad Mansoor Alam","doi":"10.1016/j.nlp.2026.100231","DOIUrl":"10.1016/j.nlp.2026.100231","url":null,"abstract":"<div><div>The rapid adoption of large language models (LLMs) and generative artificial intelligence (AI) systems has brought AI hallucinations to the forefront of contemporary natural language processing research. AI hallucinations refer to statements made by the system which are either factually incorrect, illogical, or not substantiated by any credible sources but sound coherent and fluent on the face of it. These behaviors raise serious problems when using AI in high-stakes applications such as medicine, teaching, legal matters, finance, or science, where accurate facts are required. The present survey aims to provide an extensive overview of current techniques employed to detect, verify, mitigate, or correct hallucinations in generative AI models. Initially, we provide a taxonomy of different types of AI hallucinations. Subsequently, we examine major detection and verification paradigms, including retrieval-augmented generation (RAG), factuality evaluation metrics, automated fact-checking, natural language inference (NLI), knowledge-grounded reasoning, multi-model verification, and self-reflection-based approaches. The survey further reviews widely used benchmark datasets, evaluation frameworks, and emerging multi-layer verification architectures for improving AI trustworthiness. In addition, enabling technologies such as transformer-based language models, dense retrieval systems, knowledge graphs, credibility assessment mechanisms, and truth-scoring frameworks are analyzed from the perspective of reliable content verification. Lastly, we highlight important limitations of existing approaches along with potential future research directions, such as scaling verification, multilingual reasoning, real-time verification, and privacy-enhanced trusted artificial intelligence systems. In summary, this survey offers a comprehensive guide to researchers and developers aiming to design reliable, interpretable, and trustworthy generative AI models.</div></div>","PeriodicalId":100944,"journal":{"name":"Natural Language Processing Journal","volume":"16 ","pages":"Article 100231"},"PeriodicalIF":0.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148949872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intelligent evaluation method of teaching effect of advanced mathematics based on 3D image technology","authors":"Benhua Qiu ,&nbsp;Qinghua Yao","doi":"10.1016/j.nlp.2026.100211","DOIUrl":"10.1016/j.nlp.2026.100211","url":null,"abstract":"<div><div>The present study investigates the effect of incorporating 3D image technology into teaching Advanced Mathematics (AMs) at Vocational Colleges (VCs) to help improve students' academic performance, participation, and conceptual understanding. Traditional teaching methods are always deficient in satisfying individual learning needs. This is evidenced by reduced student interest and poor consistency in Learning Outcomes (LOs). In light of this, the subsequent research will consider the comparison of conventional methods with those facilitated by support using Three-Dimensional (3D) tools such as Augmented Reality (AR)/Virtual Reality (VR) headsets and interactive displays. The findings show that students better understand complicated mathematical concepts using 3D image technology, think more positively about the subject, and are academically promoted. Moreover, an intelligent Evaluation System (ES) used eye-tracking technology to judge the effectiveness of the enhancement methodology applied using 3D This system provides an objective analysis of students' cognitive engagements and their LOs. The study thus concludes that 3D image technology has great potential to revolutionize AMs teaching by making abstract concepts more accessible and engaging.</div></div>","PeriodicalId":100944,"journal":{"name":"Natural Language Processing Journal","volume":"15 ","pages":"Article 100211"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148178411","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reading between the lines: Emotion classification using sentence transformers","authors":"Geeta Pattun,&nbsp;Pradeep Kumar","doi":"10.1016/j.nlp.2026.100213","DOIUrl":"10.1016/j.nlp.2026.100213","url":null,"abstract":"<div><div>Traditional feature extraction methods have struggled to capture nuanced emotional expressions while, transformer-based approaches although more effective often depends on computationally expensive fine-tuning. To address these issues, this research explores the use of Sentence Transformer embeddings as fixed feature extractors for emotion classification tasks. Experiments were conducted on ISEAR benchmark dataset, where ML classifiers were trained on embedding generated from four base pre-trained sentence transformers. The evaluation was performed using both holdout and Stratified 5-Fold cross-validation strategies. Among the tested approaches, using stratified 5-fold cross validation, the E5-base v2 (BERT Based) embedding model combined with a support vector classifier achieved an accuracy of 69.90%, outperforming proprietary embedding baselines without the need for fine tuning. Statistical validation performed using paired t-tests further confirmed that performance differences between embedding models are significant and not attributed to random data partitioning. Additionally, empirical finding indicates that embedding fusion of BERT based pre trained sentence transformers (BGE, E5, and GTE) performance is marginally comparable to that of classifier trained on individual embedding using same evaluation strategy. These results demonstrate that open-source sentence embeddings constitute a computationally efficient and competitive alternative to fine-tuned transformer models for emotion classification tasks.</div></div>","PeriodicalId":100944,"journal":{"name":"Natural Language Processing Journal","volume":"15 ","pages":"Article 100213"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148178412","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A comparative study of prompt-based few-shot learning for Arabic and code-switched NLP","authors":"Caroline Sabty ,&nbsp;Mohamed Eyad ,&nbsp;Alia El Bolock","doi":"10.1016/j.nlp.2026.100212","DOIUrl":"10.1016/j.nlp.2026.100212","url":null,"abstract":"<div><div>Zero-shot and few-shot learning methods offer effective solutions to data scarcity in Natural Language Processing (NLP), especially for under-resourced languages like Arabic and for mixed-language (code-switched) text. However, their capabilities in such challenging settings remain underexplored. In this work, we present the first systematic comparison of two prompt-based few-shot learning approaches; Pattern-Exploiting Training (PET) and LM-BFF (Better Few-Shot Fine-Tuning) on Arabic and Arabic-English code-switched classification tasks (Named Entity Recognition and Sentiment Analysis). Our experiments show that LM-BFF achieves a new state-of-the-art result on an Arabic-English sentiment dataset (ArEnSA), outperforming the previous best F1-score by approximately 1.5% points, and consistently surpasses PET in sentiment classification, particularly under code-switched conditions. Meanwhile, PET remains a strong baseline for structured tasks such as Arabic NER. These findings highlight the efficacy of prompt-based fine-tuning in low-resource, multilingual NLP, and underscore the potential of such techniques for underrepresented languages and code-switched text.</div></div>","PeriodicalId":100944,"journal":{"name":"Natural Language Processing Journal","volume":"15 ","pages":"Article 100212"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148241331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From word to sentence embedding and beyond: Bridging the gap in text representation survey","authors":"Peter K. Joseph,&nbsp;Victor Odumuyiwa","doi":"10.1016/j.nlp.2026.100214","DOIUrl":"10.1016/j.nlp.2026.100214","url":null,"abstract":"<div><div>Text representation is fundamental to Natural Language Processing (NLP), as it transforms human language into numbers (vectors) that machines can interpret. The quality of the representation directly determines the effectiveness of downstream tasks such as sentiment analysis, translation, and question answering. This survey adopts a multidimensional evaluation framework to analyze text representation techniques, focusing on their evolution, performance, and applications across various NLP tasks. It proposes a text representation taxonomy that categorizes embedding methods based on their granularity for specific use cases. Various evaluation metrics often used in assessing embedding performance were discussed. In addition, the survey reviewed significant data catalogues relative to their applicable NLP task, which is crucial in generating an embedding. It provides a critical analysis of emerging trends, while offering insights into their potential to address scalability and domain adaptation challenges. The paper’s unique contribution lies in its comprehensive projection of historical developments, current advancements, and future directions, providing researchers and practitioners with a simple yet valuable understanding of the field.</div></div>","PeriodicalId":100944,"journal":{"name":"Natural Language Processing Journal","volume":"15 ","pages":"Article 100214"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148241258","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Recent developments in named entity recognition techniques for Indian languages: A comprehensive review","authors":"Punam Sarmah,&nbsp;Manash Pratim Lahkar,&nbsp;Shobhanjana Kalita,&nbsp;Utpal Sharma","doi":"10.1016/j.nlp.2026.100199","DOIUrl":"10.1016/j.nlp.2026.100199","url":null,"abstract":"<div><div>This paper conducts an in-depth review of Named Entity Recognition(NER) studies in Indian languages. NER is a pivotal task in natural language processing, facilitating various downstream applications such as information extraction and sentiment analysis. However, the linguistic diversity and distinct typological features of Indian languages present unique challenges and opportunities for NER research. By employing existing literature, this review elucidates the methodologies, datasets, and evaluation metrics employed in addressing the NER task in Indian languages. It delves into the advancements, limitations, and future directions of NER techniques applied to Indian languages. This paper aims to offer researchers and practitioners a comprehensive understanding of the current landscape and challenges in NER, particularly within the context of Indian languages. This synthesis highlights the state-of-the-art and identifies potential avenues for further exploration and development in NER research in the Indian linguistic landscape.</div></div>","PeriodicalId":100944,"journal":{"name":"Natural Language Processing Journal","volume":"14 ","pages":"Article 100199"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146188629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intrinsic interpretability at parity: Attention-Based RL–MIL for student outcome prediction","authors":"Nina May Braakman ,&nbsp;Seyed Sahand Mohammadi Ziabari ,&nbsp;Ali Mohammed Mansoor Alsahag ,&nbsp;Yousuf Nasser Al Husaini","doi":"10.1016/j.nlp.2026.100204","DOIUrl":"10.1016/j.nlp.2026.100204","url":null,"abstract":"<div><div>Predicting student outcomes such as course completion or dropout is central to early intervention in online education. In this context, learning analytics systems must extract actionable insights from noisy, sequential, and weakly labeled interaction data. We revisit Reinforcement Learning-based Multiple Instance Learning (RL-MIL), a framework that learns a policy to select critical instances from bags of interactions while optimizing a downstream predictor. However, prior RL-MIL approaches rely on the Epsilon-Greedy strategy, which under-explores context and requires expensive post-hoc explanations (e.g., SHAP) to justify instance selection.</div><div>We propose two attention-based RL-MIL policies, Gated Attention and Multi-Head Attention, that directly embed context-aware selection into the policy network, providing intrinsic interpretability at inference time. Experiments on two versions of the Open University Learning Analytics Dataset (OULAD) show that while both attention-based methods match the predictive performance of the Epsilon-Greedy baseline (0.92 Macro F1 on the aggregated dataset; 0.80 on the full dataset), they reduce interpretation latency by up to two orders of magnitude and allow tunable trade-offs between sparsity and accuracy via attention-temperature control.</div><div>This work establishes the feasibility of analytical, efficient, and context-aware instance selection in weakly labeled educational settings, and provides a path toward scalable, interpretable RL-MIL frameworks that do not depend on costly post-hoc methods.</div></div>","PeriodicalId":100944,"journal":{"name":"Natural Language Processing Journal","volume":"14 ","pages":"Article 100204"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147396979","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"How robust are linguistic markers of aging? The case of aging-related social media text","authors":"Yan Cong ,&nbsp;Jingying Hu ,&nbsp;Timothy Reese ,&nbsp;Hui Liu","doi":"10.1016/j.nlp.2026.100203","DOIUrl":"10.1016/j.nlp.2026.100203","url":null,"abstract":"<div><div>Recent research suggests that linguistic biomarkers are effective in detecting probable cognitive impairment (PCI). This study extends this line of research by examining whether linguistic markers, which have been previously validated in structured, controlled clinical settings, are sufficiently robust to effectively detect PCI in unstructured, spontaneous, everyday social media posts. We hypothesized that classic linguistic markers, along with novel large language model (LLM)-derived measures, can robustly, reliably differentiate PCI from healthy controls (HC) based on social media posts. Both clinical interviews and social media posts were preprocessed to extract relevant content. We constructed natural language processing (NLP) pipelines to compute linguistic markers, and used LLMs to derive text similarity and surprisal metrics. Machine learning (ML) methods with explicit linguistic features were then used for predictive modeling. Additionally, we prompted LLM without explicit linguistic features to infer and detect PCI using zero-shot method. ML classifiers, especially gradient boosting, achieved high precision, recall, and overall accuracy in distinguishing PCI from HC across both data types (i.e., clinical interviews and social media posts). We also found that LLM-derived features and prompting methods provided moderate additional insights, and classic linguistic markers remained the most influential predictors. These findings demonstrated a reliable approach to transfer linguistic markers from clinical, controlled settings to spontaneous, unstructured social media posts. This study adds to the growing evidence that robust, AI-enhanced linguistic markers can advance early, noninvasive detection for cognitive decline in everyday language settings of social media posts.</div></div>","PeriodicalId":100944,"journal":{"name":"Natural Language Processing Journal","volume":"14 ","pages":"Article 100203"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147396897","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Kingfisher: A hybrid LLM-augmented Bangla text normalization for enhanced text-to-speech","authors":"Rajan Saha Raju,&nbsp;Arif Ahmad,&nbsp;Mohammad Shahidur Rahman","doi":"10.1016/j.nlp.2026.100201","DOIUrl":"10.1016/j.nlp.2026.100201","url":null,"abstract":"<div><div>Text normalization (TN), the process of converting non-standard words into their spoken equivalents, is a fundamental pre-processing step for text-to-speech (TTS) systems. While substantial progress has been made in TN for well-resourced languages, low-resource languages such as Bangla have received limited attention. We present Kingfisher, a three-stage hybrid framework combining LLM-based tokenization and semiotic class annotation, lexicon-driven context-aware verbalization, and error correction to build an accurate Bangla text normalizer. Experimental evaluations across diverse Bangla texts demonstrate that Kingfisher achieves superior performance, with an overall accuracy of 96<em>%</em> (confidence interval 95%<em>–97%</em>), significantly outperforming the only publicly available Bangla text normalizer, <em>Sparrowhawk</em>. To support further research, we release the Bangla text normalization dataset and make the source code of the text normalization system publicly available<span><span>¹</span></span>, offering a substantial contribution to the Bangla speech technology community.</div></div>","PeriodicalId":100944,"journal":{"name":"Natural Language Processing Journal","volume":"14 ","pages":"Article 100201"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147396978","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}