Natural Language Processing Journal最新文献

{"title":"OVALYTICS: Enhancing Offensive Video Detection with YouTube Transcriptions and Advanced Language Models","authors":"Sneha Chinivar ,&nbsp;Roopa M.S. ,&nbsp;Arunalatha J.S. ,&nbsp;Venugopal K.R.","doi":"10.1016/j.nlp.2025.100147","DOIUrl":"10.1016/j.nlp.2025.100147","url":null,"abstract":"<div><div>The exponential growth of offensive content online underscores the need for robust content moderation. In response, this work presents OVALYTICS (Offensive Video Analysis Leveraging YouTube Transcriptions with Intelligent Classification System), a comprehensive framework that introduces novel integrations of advanced technologies for offensive video detection. Unlike existing approaches, OVALYTICS uniquely combines Whisper AI for accurate audio-to-text transcription with state-of-the-art large language models (LLMs) such as BERT, ALBERT, XLM-R, MPNet, and T5 for semantic analysis. The framework also features a newly curated dataset tailored for fine-grained evaluation, achieving significant improvements in accuracy and F1-scores over traditional methods and advancing the state of automated content moderation.</div></div>","PeriodicalId":100944,"journal":{"name":"Natural Language Processing Journal","volume":"11 ","pages":"Article 100147"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143864803","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":"Understanding the effects of human-written paraphrases in LLM-generated text detection","authors":"Hiu Ting Lau,&nbsp;Arkaitz Zubiaga","doi":"10.1016/j.nlp.2025.100151","DOIUrl":"10.1016/j.nlp.2025.100151","url":null,"abstract":"<div><div>Natural Language Generation has been rapidly developing with the advent of large language models (LLMs). While their usage has sparked significant attention from the general public, it is important for readers to be aware when a piece of text is LLM-generated. This has brought about the need for building models that enable automated LLM-generated text detection, with the aim of mitigating potential negative outcomes of such content. Existing LLM-generated detectors show competitive performances in telling apart LLM-generated and human-written text, but this performance is likely to deteriorate when paraphrased texts are considered. In this study, we devise a new data collection strategy to collect Human &amp; LLM Paraphrase Collection (HLPC), a first-of-its-kind dataset that incorporates human-written texts and paraphrases, as well as LLM-generated texts and paraphrases. With the aim of understanding the effects of human-written paraphrases on the performance of SOTA LLM-generated text detectors OpenAI RoBERTa and watermark detectors, we perform classification experiments that incorporate human-written paraphrases, watermarked and non-watermarked LLM-generated documents from GPT and OPT, and LLM-generated paraphrases from DIPPER and BART. The results show that the inclusion of human-written paraphrases has a significant impact of LLM-generated detector performance, promoting TPR@1%FPR with a possible trade-off of AUROC and accuracy.</div></div>","PeriodicalId":100944,"journal":{"name":"Natural Language Processing Journal","volume":"11 ","pages":"Article 100151"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144069981","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":"Comparative analysis of Mixture-of-Agents models for natural language inference with ANLI data","authors":"Swathi Sowmya Bavirthi,&nbsp;Dama Pranati Sreya,&nbsp;Tanguturi Poojitha","doi":"10.1016/j.nlp.2025.100140","DOIUrl":"10.1016/j.nlp.2025.100140","url":null,"abstract":"<div><div>The Mixture-of-Agents (MoA) framework represents a significant contribution to artificial intelligence (AI) by enhancing the capabilities of large language models (LLMs) through the integration of multiple specialized agents. This approach addresses the limitations of traditional single-agent models, enabling more robust reasoning, improved accuracy in natural language inference (NLI), and better adaptability to diverse linguistic contexts. The key contribution to AI lies in MoA’s ability to dynamically orchestrate these agents, each focusing on different aspects of a task, leading to a more comprehensive and effective problem-solving approach. In the domain of engineering, MoA finds its application in real-time decision-making systems, particularly in autonomous systems and intelligent control environments. By deploying MoA within these systems, we demonstrate its effectiveness in enhancing precision and reliability in language-based decision-making processes. This integration significantly improves the system’s ability to adapt to dynamic scenarios, making MoA a valuable tool for bridging the gap between advanced AI methodologies and practical engineering solutions.</div></div>","PeriodicalId":100944,"journal":{"name":"Natural Language Processing Journal","volume":"11 ","pages":"Article 100140"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143686231","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":"Advances in machine transliteration methods, limitations, challenges, applications and future directions","authors":"A’la Syauqi ,&nbsp;Aji Prasetya Wibawa","doi":"10.1016/j.nlp.2025.100158","DOIUrl":"10.1016/j.nlp.2025.100158","url":null,"abstract":"<div><div>Machine transliteration is critical in natural language processing (NLP), facilitating script conversion while preserving phonetic integrity across diverse languages. Using the PRISMA framework, this review analyzes 73 selected studies on machine transliteration, covering both methodological advancements and its role in NLP applications. Among these, 37 studies focus on transliteration methods (rule-based, statistical, machine learning, hybrid, and semantic), while 32 studies explore their application in NLP tasks such as machine translation, sentiment analysis, and text normalization. Rule-based methods provide structured frameworks but face challenges in adapting to linguistic variability. Statistical techniques demonstrate robustness yet depend heavily on the availability of parallel corpora. Machine learning models leverage neural architectures to achieve high accuracy but are constrained by data scarcity for low-resource languages. Hybrid approaches integrate multiple methodologies, while semantic knowledge-based models enhance accuracy by incorporating linguistic features. The review highlights transliteration’s role in NLP applications such as machine translation, sentiment analysis, and text normalization, which are critical for improving multilingual language accessibility. Findings show that machine learning-based approaches dominate transliteration research (32 of 73 studies), followed by rule-based and hybrid methods. These approaches contribute to improving multilingual accessibility and NLP performance. This study provides actionable insights for researchers and practitioners by synthesizing advancements and identifying challenges. These insights enable the development more efficient and inclusive transliteration systems, ultimately supporting linguistic diversity and advancing multilingual NLP technologies. The review identifies gaps in addressing underrepresented languages like Javanese, where complex character sets, orthographic rules, and scriptio continua remain underexplored.</div></div>","PeriodicalId":100944,"journal":{"name":"Natural Language Processing Journal","volume":"11 ","pages":"Article 100158"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144230930","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":"GPT-4o in radiology: In-context learning based automatic generation of radiology impressions","authors":"Mohammed Mahyoub ,&nbsp;Yong Wang ,&nbsp;Mohammad T. Khasawneh","doi":"10.1016/j.nlp.2025.100145","DOIUrl":"10.1016/j.nlp.2025.100145","url":null,"abstract":"<div><div>Translating radiological findings into clinical impressions is critical for effective medical communication but is often labor-intensive and prone to variability. This study investigates the potential of the GPT-4o large language model (LLM) to automate the generation of radiology impressions from reports, using in-context learning techniques to improve accuracy. Using the MIMIC-IV-CXR dataset, the study compares three generative AI approaches: zero-shot generation (ZS), in-context learning with random examples (ICLR), and in-context learning with semantic nearest neighbors (ICLSN). These methods were evaluated using text summarization metrics such as BERT Score, ROUGE, and METEOR. Statistical tests, including the Kruskal–Wallis and Mann–Whitney U tests, were employed to validate the results. The ICLSN approach significantly outperformed ZS and ICLR, achieving the highest precision (0.9002 ± 0.0471), recall (0.8914 ± 0.0501), and F1 scores (0.8952 ± 0.0432) according to BERT Score. ROUGE and METEOR metrics confirmed these findings, with ICLSN showing notable improvements in ROUGE-1, ROUGE-2, and ROUGE-L scores (0.4673 ± 0.2606, 0.3130 ± 0.2863, and 0.4198 ± 0.2674, respectively). METEOR scores also improved significantly with ICLSN (0.4448 ± 0.2804). The study demonstrates that GPT-4o, particularly when using semantic nearest neighbors for in-context learning, can effectively generate clinically relevant radiology impressions. The method enhances the accuracy and reliability of automated clinical text summarization, suggesting a valuable tool for improving the efficiency and consistency of radiological assessments. Future work should explore fine-tuning to further optimize these outcomes and extend applications to other clinical texts.</div></div>","PeriodicalId":100944,"journal":{"name":"Natural Language Processing Journal","volume":"11 ","pages":"Article 100145"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143833343","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":"Financial sentiment analysis for pre-trained language models incorporating dictionary knowledge and neutral features","authors":"Yongyong Sun,&nbsp;Haiping Yuan,&nbsp;Fei Xu","doi":"10.1016/j.nlp.2025.100148","DOIUrl":"10.1016/j.nlp.2025.100148","url":null,"abstract":"<div><div>With increasing financial market complexity, accurate sentiment analysis of financial texts has become crucial. Traditional methods often misinterpret financial terminology and show high error rates in neutral sentiment recognition. This study aims to improve financial sentiment analysis accuracy through developing EnhancedFinSentiBERT, a model incorporating financial domain pre-training, dictionary knowledge embedding, and neutral feature extraction. Experiments on the FinancialPhraseBank, FiQA and Headline datasets demonstrate the model’s superior performance compared to mainstream methods, particularly in neutral sentiment recognition. Ablation analysis reveals that dictionary knowledge embedding and neutral feature extraction contribute most significantly to model improvement.</div></div>","PeriodicalId":100944,"journal":{"name":"Natural Language Processing Journal","volume":"11 ","pages":"Article 100148"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143869774","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 multi-way parallel named entity annotated corpus for English, Tamil and Sinhala","authors":"Surangika Ranathunga ,&nbsp;Asanka Ranasinghe ,&nbsp;Janaka Shamal ,&nbsp;Ayodya Dandeniya ,&nbsp;Rashmi Galappaththi ,&nbsp;Malithi Samaraweera","doi":"10.1016/j.nlp.2025.100160","DOIUrl":"10.1016/j.nlp.2025.100160","url":null,"abstract":"<div><div>This paper presents a multi-way parallel English-Tamil-Sinhala corpus annotated with Named Entities (NEs), where Sinhala and Tamil are low-resource languages. Using pre-trained multilingual Language Models (mLMs), we establish new benchmark Named Entity Recognition (NER) results on this dataset for Sinhala and Tamil. We also carry out a detailed investigation on the NER capabilities of different types of LMs. Finally, we demonstrate the utility of our NER system on a low-resource Neural Machine Translation (NMT) task. Our dataset is publicly released: <span><span>https://github.com/suralk/multiNER</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":100944,"journal":{"name":"Natural Language Processing Journal","volume":"11 ","pages":"Article 100160"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144230939","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":"Categorizing Mental Stress: A Consistency-Focused Benchmarking of ML and DL Models for Multi-Label, Multi-Class Classification via Taxonomy-Driven NLP Techniques","authors":"Juswin Sajan John ,&nbsp;Boppuru Rudra Prathap ,&nbsp;Gyanesh Gupta ,&nbsp;Jaivanth Melanaturu","doi":"10.1016/j.nlp.2025.100162","DOIUrl":"10.1016/j.nlp.2025.100162","url":null,"abstract":"<div><div>Mental stress, a critical concern worldwide, necessitates precise and nuanced characterization. This study introduces a novel approach to effectively characterize mental stress through a multi-label, multi-class classification framework through natural language processing techniques. Building on existing literature, discussions with psychologists and other mental health practitioners, we developed a taxonomy of 27 distinctive markers spread across 4 label categories; aiming to create a preliminary screening tool leveraging textual data.</div><div>The core objective is to identify the most suitable model for this complex task, encompassing comprehensive evaluation of various machine learning and deep learning algorithms. we experimented with support vector machines (SVM), random forest (RF) and long short-term memory (LSTM) algorithms incorporating various feature combinations involving Term Frequency-Inverse Document Frequency (TF-IDF) and Latent Dirichlet Allocation (LDA). The best performer of this comparative study was further evaluated against an LLM.</div><div>The potential of large language models (LLMs), including their language understanding and prediction capabilities, is another key focus. We explore how these models could augment and advance mental health research, offering new perspectives and insights into the characterization of mental stress.</div><div>Our findings show that the top model, an LSTM with TF-IDF and LDA (class weights assigned) outperformed the PaLM model with a coefficient of variation as low as 0.87% across all labels. Despite the PaLM model’s superior average performance, it exhibited higher variability among different labels.</div></div>","PeriodicalId":100944,"journal":{"name":"Natural Language Processing Journal","volume":"11 ","pages":"Article 100162"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144242689","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":"Quantifying extreme opinions on Reddit amidst the 2023 Israeli–Palestinian conflict","authors":"Alessio Guerra ,&nbsp;Marcello Lepre ,&nbsp;Oktay Karakuş","doi":"10.1016/j.nlp.2025.100156","DOIUrl":"10.1016/j.nlp.2025.100156","url":null,"abstract":"<div><div>This study investigates the dynamics of extreme opinions on social media during the 2023 Israeli–Palestinian conflict, utilising a comprehensive dataset of over 450,000 posts from four Reddit subreddits (<em>r/Palestine</em>, <em>r/Judaism</em>, <em>r/IsraelPalestine</em>, and <em>r/worldnews</em>). A lexicon-based, unsupervised methodology was developed to measure “extreme opinions” by considering factors such as anger, polarity, and subjectivity. The analysis identifies significant peaks in extremism scores that correspond to pivotal real-life events, such as the IDF’s bombings of Al Quds Hospital and the Jabalia Refugee Camp, and the end of a ceasefire following a terrorist attack. Additionally, this study explores the distribution and correlation of these scores across different subreddits and over time, providing insights into the propagation of polarised sentiments in response to conflict events. By examining the quantitative effects of each score on extremism and analysing word cloud similarities through Jaccard indices, the research offers a nuanced understanding of the factors driving extreme online opinions. Our findings show that posts exhibiting extreme sentiment surged up to 80% (an increase of 0.3 in extremism score above the average of 0.405 at the end of October) during key conflict events. Compared to recent studies that have not explicitly quantified extremism in an unsupervised manner, we contribute to the literature by addressing this gap through a novel extremism score, derived from sentiment polarity, anger, and subjectivity, to analyse Reddit discourse surrounding the 2023 Israel–Palestine conflict. This approach captures the complex interplay between real-world events and online reactions, while acknowledging the inherent challenges of measuring extremism in dynamic social media environments. Our approach also enables scalable monitoring of public sentiment extremity, providing valuable insights for policymakers and conflict researchers.</div></div>","PeriodicalId":100944,"journal":{"name":"Natural Language Processing Journal","volume":"11 ","pages":"Article 100156"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144147094","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":"Bayesian Q-learning in multi-objective reward model for homophobic and transphobic text classification in low-resource languages: A hypothesis testing framework in multi-objective setting","authors":"Vivek Suresh Raj ,&nbsp;Ruba Priyadharshini ,&nbsp;Saranya Rajiakodi ,&nbsp;Bharathi Raja Chakravarthi","doi":"10.1016/j.nlp.2025.100152","DOIUrl":"10.1016/j.nlp.2025.100152","url":null,"abstract":"<div><div>Most Reinforcement Learning (RL) algorithms optimize a single-objective function, whereas real-world decision-making involves multiple aspects. For hate comment classification, an agent must balance maximizing the F1-score while minimizing False Positives (FP) to enhance precision and reduce misclassifications. However, such multi-objective optimization introduces uncertainties in decision-making. To address this, we propose a Bayesian Q-Learning framework with a convolutional neural network policy. The policy outputs action logits, integrated with Q-value estimates sampled via Thompson Sampling from a Gaussian posterior. Our reward function combines F1-score (objective 1) and a penalty for misclassification (objective 2) to optimize learning. To validate our framework, firstly we show that our framework classifies the hate-comments comparatively better than other baselines by scoring an F1-score of 83%, 93%, 77% and 71% in English-Tamil, English, Kannada and Malayalam datasets for detecting homophobic and transphobic comments respectively. Secondly, we demonstrate that the variance of Q-value estimates in our Bayesian posterior decreases significantly over time, indicating that the agent has learned an optimal policy that effectively balances the competing objectives. This finding is further supported by statistical t-tests conducted across all datasets, which confirm the significance of the observed variance reduction. Additionally, we observe our agent’s multi-objective optimization path in 3D space, which shows its ability to balance reward (F1-score) and regret. Furthermore, we compare the action selection between our Bayesian approach and non-Bayesian action clustering using K-Means algorithms, where our analysis highlights coherent clustering which indicates structure exploration, while non-Bayesian approach shows premature convergence to suboptimal policies.</div></div>","PeriodicalId":100944,"journal":{"name":"Natural Language Processing Journal","volume":"11 ","pages":"Article 100152"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143924105","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}