Natural Language Processing Journal最新文献

{"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":"2025-04-23","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":"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":"2025-04-21","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":"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":"2025-04-12","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":"Detecting cognitive engagement in online course forums: A review of frameworks and methodologies","authors":"Nazmus Sakeef,&nbsp;M. Ali Akber Dewan,&nbsp;Fuhua Lin,&nbsp;Dharamjit Parmar","doi":"10.1016/j.nlp.2025.100146","DOIUrl":"10.1016/j.nlp.2025.100146","url":null,"abstract":"<div><div>A key aspect of online learning in higher education involves the utilization of course discussion forums. Assessing the quality of posts, such as cognitive engagement, within online course discussion forums, and determining students’ interest and participation is challenging yet beneficial. This research investigates existing literature on identifying the cognitive engagement of online learners through the analysis of course discussion forums. Essentially, this review examines three educational frameworks - <em>Van Der Meijden’s Knowledge Construction in Synchronous and Asynchronous Discussion Posts (KCSA), Community of Inquiry (CoI), and Interactive, Constructive, Active, and Passive (ICAP)</em>, which have been widely used for students’ cognitive engagement detection analyzing their posts in course discussion forums. This study also examines the natural language processing and deep learning approaches employed and integrated with the above three educational frameworks in the existing literature concerning the detection of cognitive engagement in the context of online learning. The article provides recommendations for enhancing instructional design and fostering student engagement by leveraging cognitive engagement detection. This research underscores the significance of automating the identification of cognitive engagement in online learning and puts forth suggestions for future research directions.</div></div>","PeriodicalId":100944,"journal":{"name":"Natural Language Processing Journal","volume":"11 ","pages":"Article 100146"},"PeriodicalIF":0.0,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143848527","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":"Precise length control for large language models","authors":"Bradley Butcher,&nbsp;Michael O’Keefe,&nbsp;James Titchener","doi":"10.1016/j.nlp.2025.100143","DOIUrl":"10.1016/j.nlp.2025.100143","url":null,"abstract":"<div><div>Large Language Models (LLMs) are increasingly used in production systems, powering applications such as chatbots, summarization, and question answering. Despite their success, controlling the length of their response remains a significant challenge, particularly for tasks requiring brevity or specific levels of detail. In this work, we propose a method to adapt pre-trained decoder-only LLMs for precise control of response length. Our approach incorporates a secondary length-difference positional encoding (LDPE) into the input embeddings, which counts down to a user-set response termination length. Fine-tuning with LDPE allows the model to learn to terminate responses coherently at the desired length, achieving mean token errors of less than 3 tokens. We also introduce Max New Tokens++, an extension that enables flexible upper-bound length control, rather than an exact target. Experimental results on tasks such as question answering and document summarization demonstrate that our method enables precise length control without compromising response quality.</div></div>","PeriodicalId":100944,"journal":{"name":"Natural Language Processing Journal","volume":"11 ","pages":"Article 100143"},"PeriodicalIF":0.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143800015","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":"The fine art of fine-tuning: A structured review of advanced LLM fine-tuning techniques","authors":"Samar Pratap ,&nbsp;Alston Richard Aranha ,&nbsp;Divyanshu Kumar ,&nbsp;Gautam Malhotra ,&nbsp;Anantharaman Palacode Narayana Iyer ,&nbsp;Shylaja S.S.","doi":"10.1016/j.nlp.2025.100144","DOIUrl":"10.1016/j.nlp.2025.100144","url":null,"abstract":"<div><div>Transformer-based models have consistently demonstrated superior accuracy compared to various traditional models across a range of downstream tasks. However, due to their large nature, training or fine-tuning them for specific tasks has heavy computational and memory demands. This causes the creation of specialized transformer-based models to be almost impossible in the generally present constrained scenarios. To tackle this issue and to make these large models more accessible, a plethora of techniques have been developed. In this study, we will be reviewing the types of techniques developed, their impacts and benefits concerning performance and resource usage along with the latest developments in the domain. We have broadly categorized these techniques into six key areas: Changes in Training Method, Changes in Adapter, Quantization, Parameter Selection, Mixture of Experts, and Application based methods. We collated the results of various techniques on common benchmarks and also evaluated their performance on different datasets and base models.</div></div>","PeriodicalId":100944,"journal":{"name":"Natural Language Processing Journal","volume":"11 ","pages":"Article 100144"},"PeriodicalIF":0.0,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143738563","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":"LLMs for product classification in e-commerce: A zero-shot comparative study of GPT and claude models","authors":"Konstantinos I. Roumeliotis ,&nbsp;Nikolaos D. Tselikas ,&nbsp;Dimitrios K. Nasiopoulos","doi":"10.1016/j.nlp.2025.100142","DOIUrl":"10.1016/j.nlp.2025.100142","url":null,"abstract":"<div><div>In the rapidly evolving e-commerce landscape, efficient and accurate product classification is essential for enhancing customer experience and streamlining operations. Traditional product classification methods, which depend heavily on labeled data and manual effort, struggle with scalability and adaptability to diverse product categories. This study explores the transformative potential of large language models (LLMs) for zero-shot product classification in e-commerce, addressing the challenge of automating product categorization without prior labeled training data. We evaluate the performance of four state-of-the-art LLMs — GPT-4o, GPT-4o mini, Claude 3.5 Sonnet, and Claude 3.5 Haiku — on a diverse dataset of 248 product categories, each containing 20 samples, structured into 8 subsets. Each model performs zero-shot classification, assigning products to predefined categories without prior exposure. Our findings reveal significant variations in classification accuracy across models, with certain LLMs demonstrating superior scalability and adaptability for real-world e-commerce applications. Based on these insights, we developed an API software to integrate the top-performing models into e-commerce systems, enhancing automation and efficiency. This study underscores the transformative role of LLMs in revolutionizing e-commerce workflows and recommends their adoption for scalable, intelligent product classification.</div></div>","PeriodicalId":100944,"journal":{"name":"Natural Language Processing Journal","volume":"11 ","pages":"Article 100142"},"PeriodicalIF":0.0,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143724168","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":"OdNER: NER resource creation and system development for low-resource Odia language","authors":"Tusarkanta Dalai ,&nbsp;Anupam Das ,&nbsp;Tapas Kumar Mishra ,&nbsp;Pankaj Kumar Sa","doi":"10.1016/j.nlp.2025.100139","DOIUrl":"10.1016/j.nlp.2025.100139","url":null,"abstract":"<div><div>This work aims to enhance the usability of natural language processing (NLP) based systems for the low-resource Odia language by focusing on the development of effective named entity recognition (NER) system. NLP applications rely heavily on NER to extract relevant information from massive amounts of unstructured text. The task of identifying and classifying the named entities included in a given text into a set of predetermined categories is referred to as NER. Already, the NER task has accomplished productive results in English as well as in a number of other European languages. On the other hand, because of a lack of supporting tools and resources, it has not yet been thoroughly investigated in Indian languages, particularly the Odia language. Recently, approaches based on machine learning (ML) and deep learning (DL) have demonstrated exceptional performance when it comes to constructing NLP tasks. Moreover, transformer models, particularly masked-language models (MLM), have demonstrated remarkable efficacy in the NER task; nevertheless, these methods generally call for massive volumes of annotated corpus. Unfortunately, we could not find any open-source NER corpus for the Odia language. The purpose of this research is to compile OdNER, a NER dataset with quality baselines for the low-resource Odia language. The Odia NER corpus OdNER contains 48,000 sentences having 6,71,354 tokens and 98,116 name entities annotated with 12 tags. To establish the quality of our corpus, we use conditional random field (CRF) and BiLSTM model as our baseline models. To demonstrate the efficacy of our dataset, we conduct a comparative evaluation of various transformer-based multilingual language models (IndicBERT, MuRIL, XLM-R) and utilize them to carry out the sequence labeling task for NER. With the pre-trained XLM-R multilingual model, our dataset achieves a maximum F1 score of 90.48%. When it comes to Odia NER, no other work comes close to matching the quality and quantity of ours. We anticipate that, this work will have made substantial progress toward the development of NLP tasks for the Odia language.</div></div>","PeriodicalId":100944,"journal":{"name":"Natural Language Processing Journal","volume":"11 ","pages":"Article 100139"},"PeriodicalIF":0.0,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143644136","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":"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":"2025-03-18","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":"BeliN: A novel corpus for Bengali religious news headline generation using contextual feature fusion","authors":"Md Osama ,&nbsp;Ashim Dey ,&nbsp;Kawsar Ahmed ,&nbsp;Muhammad Ashad Kabir","doi":"10.1016/j.nlp.2025.100138","DOIUrl":"10.1016/j.nlp.2025.100138","url":null,"abstract":"<div><div>Automatic text summarization, particularly headline generation, remains a critical yet under-explored area for Bengali religious news. Existing approaches to headline generation typically rely solely on the article content, overlooking crucial contextual features such as sentiment, category, and aspect. This limitation significantly hinders their effectiveness and overall performance. This study addresses this limitation by introducing a novel corpus, BeliN (Bengali Religious News) – comprising religious news articles from prominent Bangladeshi online newspapers, and <em>MultiGen</em> – a contextual multi-input feature fusion headline generation approach. Leveraging transformer-based pre-trained language models such as BanglaT5, mBART, mT5, and mT0, <em>MultiGen</em> integrates additional contextual features – including category, aspect, and sentiment – with the news content. This fusion enables the model to capture critical contextual information often overlooked by traditional methods. Experimental results demonstrate the superiority of <em>MultiGen</em> over the baseline approach that uses only news content, achieving a BLEU score of 18.61 and ROUGE-L score of 24.19, compared to baseline approach scores of 16.08 and 23.08, respectively. These findings underscore the importance of incorporating contextual features in headline generation for low-resource languages. By bridging linguistic and cultural gaps, this research advances natural language processing for Bengali and other under-represented languages. To promote reproducibility and further exploration, the dataset and implementation code are publicly accessible at <span><span>https://github.com/akabircs/BeliN</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":100944,"journal":{"name":"Natural Language Processing Journal","volume":"11 ","pages":"Article 100138"},"PeriodicalIF":0.0,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143629386","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}