Natural Language Processing Journal最新文献

{"title":"Fuse-MD: A culturally-aware multimodal model for detecting misogyny memes","authors":"Rahul Ponnusamy ,&nbsp;Saranya Rajiakodi ,&nbsp;Bhuvaneswari Sivagnanam ,&nbsp;Anshid Kizhakkeparambil ,&nbsp;Dhruv Sharma ,&nbsp;Paul Buitelaar ,&nbsp;Bharathi Raja Chakravarthi","doi":"10.1016/j.nlp.2026.100197","DOIUrl":"10.1016/j.nlp.2026.100197","url":null,"abstract":"<div><div><strong>Warning:</strong> This study involves an analysis of meme content that may contain offensive or upsetting material, used only for research and illustrative purposes. The data presented does not represent the views or convictions of the authors or their associated organizations. The emergence of social media has transformed global communication, enabling cultural expression and the exchange of ideas across platforms such as Facebook, X, and Instagram. This openness has facilitated the dissemination of harmful content, including misogyny, which surprisingly appears in the form of memes, widely circulated online images embedded in text that convey humor or commentary. Misogynistic memes often mock or criticize women’s experiences, perpetuate negative stereotypes, reinforce gender discrimination, and promote violence. Detecting such content is particularly challenging in low-resource languages such as Tamil and Malayalam, due to the limited availability of linguistic resources and tools. This study introduces the Misogyny Detection Meme Dataset (MDMD), the first multimodal dataset specifically curated for detecting misogyny memes in Tamil and Malayalam. We further conducted a shared task using MDMD and established baselines for both languages. This study proposes the Fusion-based Multimodal Framework for Misogyny Meme Detection (Fuse-MD), which employs a transfer learning approach to identify misogynistic memes in low-resource languages. Comparative analysis shows that element-wise fusion performs best for Tamil, while gated fusion is optimal for Malayalam, highlighting the role of cultural and linguistic factors in fusion design. We also introduce a “Threshold Optimization Technique via Macro-F1 Calibration,” which calibrates prediction thresholds on the development set and helps balance the accuracy loss caused by quantization. Experimental results demonstrate that Fuse-MD outperforms the shared task baseline, providing a robust framework for detecting misogyny memes in low-resource languages and laying the foundation for future research.</div></div>","PeriodicalId":100944,"journal":{"name":"Natural Language Processing Journal","volume":"14 ","pages":"Article 100197"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037675","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":"Uzbek language morphology analyser","authors":"Nikita Murzintcev ,&nbsp;Shakhlo Shukurlaevna Yuldasheva","doi":"10.1016/j.nlp.2025.100195","DOIUrl":"10.1016/j.nlp.2025.100195","url":null,"abstract":"<div><div>Although Uzbek is the official language of Uzbekistan, it remains one of the low-resource languages. In this paper, we propose a morphology analyser based on the Hunspell library. Such a tool cannot be developed without a detailed description of the Uzbek language, so the main part of the paper is dedicated to studying Uzbek morphology in a way sufficient for building morphology-analysis and spell-checking software. A special emphasis is placed on listing all possible word forms that can be produced with inflectional affixes, accounting for irregular forms, and phonetic assimilation. The orthography of the language is provided in accordance with the latest reforms in the script system and supplemented with transliteration rules and recommendations for text normalization for computer processing. The produced lemmatizer is compatible with a wide range of existing software. The results of this project include a dictionary of Uzbek lemmas annotated with parts of speech tags.</div></div>","PeriodicalId":100944,"journal":{"name":"Natural Language Processing Journal","volume":"14 ","pages":"Article 100195"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145771853","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":"Demystifying chatgpt: How it masters genre recognition","authors":"Subham Raj ,&nbsp;Sriparna Saha ,&nbsp;Brijraj Singh ,&nbsp;Niranjan Pedanekar","doi":"10.1016/j.nlp.2026.100198","DOIUrl":"10.1016/j.nlp.2026.100198","url":null,"abstract":"<div><div>The emergence of ChatGPT has drawn considerable attention in the NLP community for its impressive performance across a wide range of language tasks. However, its effectiveness in multi-label movie genre prediction remains underexplored. This study evaluates the genre prediction capabilities of multiple Large Language Models (LLMs), including ChatGPT, using the MovieLens-100K dataset comprising 1682 movies spanning 18 genres. We investigate zero-shot and few-shot prompting strategies based on movie trailer transcripts and subtitles, where each movie may belong to multiple genres. Our results show that ChatGPT consistently outperforms earlier LLM baselines under both zero-shot and few-shot settings, while instruction fine-tuning further improves recall and overall predictive coverage. To explore multimodal extensions, we augment textual prompts with visual cues extracted from movie posters using a Vision-Language Model (VLM). While the incorporation of visual information yields selective, genre-dependent benefits–particularly improving recall for visually distinctive genres–the overall gains in aggregate performance metrics remain limited. Overall, our findings highlight the robustness of prompt-based and fine-tuned LLMs for genre prediction, and suggest that multimodal information can provide complementary signals in specific cases, motivating future work on tighter task-aligned vision-language integration.</div></div>","PeriodicalId":100944,"journal":{"name":"Natural Language Processing Journal","volume":"14 ","pages":"Article 100198"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146078198","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":"Enhancing Retrieval-Augmented Generation with topic-enriched embeddings: A hybrid approach integrating traditional NLP techniques","authors":"Rodrigo Kataishi","doi":"10.1016/j.nlp.2026.100200","DOIUrl":"10.1016/j.nlp.2026.100200","url":null,"abstract":"<div><div>Retrieval-Augmented Generation (RAG) systems depend on precise document retrieval to integrate external knowledge into large language models (LLMs). However, ensuring retrieval accuracy remains a challenge, particularly in corpora with overlapping topics and thematic diversity. This study introduces the concept of topic-enriched embeddings, which combine traditional term-frequency methods and advanced topic modeling techniques-such as TF-IDF, Latent Semantic Analysis (LSA), and Latent Dirichlet Allocation (LDA)-with modern SOTA contextual embeddings (all-minilm). Topic-enriched embeddings capture both term-level and topic-level semantics, using latent topic structures and dimensionality reduction to improve semantic clustering, retrieval precision, and computational efficiency. Using a legal text dataset, the proposed method demonstrates superior performance across clustering coherence and retrieval metrics. These findings underscore the potential of topic-enriched embeddings as a foundational component to improve knowledge-intensive RAG systems.</div></div>","PeriodicalId":100944,"journal":{"name":"Natural Language Processing Journal","volume":"14 ","pages":"Article 100200"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147396976","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":"Enhancing language models with boosting and targeted fine-tuning for real-word error detection","authors":"Corina Masanti ,&nbsp;Hans-Friedrich Witschel ,&nbsp;Kaspar Riesen","doi":"10.1016/j.nlp.2026.100202","DOIUrl":"10.1016/j.nlp.2026.100202","url":null,"abstract":"<div><div>Over the past years, extensive research has led to significant advancements in tools for the automatic detection and correction of errors in documents. Despite this progress, several challenges remain unresolved. In particular, the identification of real-word errors – errors involving words that are grammatically valid but contextually inappropriate within a given sentence – continues to pose a considerable difficulty. Addressing such errors requires models with a sophisticated understanding of linguistic context. Transformer-based language models are particularly well-suited for this task due to their contextual modeling capabilities. To further enhance their performance, we propose a boosting-based training approach in conjunction with a synthetically generated data set created via pattern-based noise injection. We evaluate this method across three transformer-based architectures, viz. mBERT, LLaMA 3, and Mistral 7B. Our experimental results show that the boosting-based strategy consistently improves real-word error detection across all models. A subsequent in-depth error analysis reveals limitations in the synthetic training data, prompting the development of a targeted fine-tuning procedure designed to address these shortcomings and further optimize model performance. A comparison with prompt-based inference using a large language model demonstrates that specialized, fine-tuned models yield more reliable performance for this task. Finally, an evaluation under realistic class imbalance highlights practical trade-offs between ranking quality and threshold-based detection, particularly for rare error types.</div></div>","PeriodicalId":100944,"journal":{"name":"Natural Language Processing Journal","volume":"14 ","pages":"Article 100202"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147396977","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":"Adversarial distractor generation for MCQA: Leveraging in-context learning and rule-based approaches","authors":"Gulsum Yigit ,&nbsp;Mehmet Fatih Amasyali","doi":"10.1016/j.nlp.2025.100186","DOIUrl":"10.1016/j.nlp.2025.100186","url":null,"abstract":"<div><div>Adversarial distractor generation strengthens the capability of Multiple Choice Question Answering (MCQA) systems. This study introduces two distinct methods: in-context learning and rule-based techniques, which are designed explicitly for adversarial distractor generation for MCQA datasets. We leverage the LLama-7b model in the in-context learning approach, following a prompt-based strategy for adversarial distractor generation. This methodology is designed to enrich the variety and complexity of the distractors. Besides, this study presents a rule-based approach, mainly focusing on the impact of appending garbage values to the answer options. Via comprehensive experiments, we systematically evaluate the results of two approaches on the T5-Base and RoBERTa-Large across three MCQA datasets. The empirical results demonstrate a constant decrease in performance across all evaluated datasets when utilizing both the in-context learning and rule-based approaches.</div></div>","PeriodicalId":100944,"journal":{"name":"Natural Language Processing Journal","volume":"13 ","pages":"Article 100186"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145417641","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":"Bridging gaps in natural language processing for Yorùbá: A systematic review of a decade of progress and prospects","authors":"Toheeb Aduramomi Jimoh,&nbsp;Tabea De Wille,&nbsp;Nikola S. Nikolov","doi":"10.1016/j.nlp.2025.100194","DOIUrl":"10.1016/j.nlp.2025.100194","url":null,"abstract":"<div><div>Natural Language Processing (NLP) is becoming a dominant subset of artificial intelligence as the need to help machines understand human language becomes indispensable. Several NLP applications are ubiquitous, partly due to the myriad datasets being churned out daily through mediums like social networking sites. However, the growing development has not been evident in most African languages due to the persisting resource limitations, among other issues. Yorùbá language, a tonal and morphologically rich African language, suffers a similar fate, resulting in limited NLP usage. To encourage further research towards improving this situation, this systematic literature review aims to comprehensively analyse studies addressing NLP development for Yorùbá, identifying challenges, resources, techniques, and applications. A well-defined search string from a structured protocol was employed to search, select, and analyse 105 primary studies between 2014 and 2024 from reputable databases. The review highlights the scarcity of annotated corpora, the limited availability of pre-trained language models (PLMs), and linguistic challenges like tonal complexity and diacritic dependency as significant obstacles. It also revealed the prominent techniques, including rule-based methods, statistical methods, deep learning, and transfer learning, which were implemented alongside datasets of Yorùbá speech corpora, among others. The findings reveal a growing body of multilingual and monolingual resources, even though the field is constrained by socio-cultural factors such as code-switching and the desertion of language for digital usage. This review synthesises existing research, providing a foundation for advancing NLP for Yorùbá and in African languages generally. It aims to guide future research by identifying gaps and opportunities, thereby contributing to the broader inclusion of Yorùbá and other under-resourced African languages in global NLP advancements.</div></div>","PeriodicalId":100944,"journal":{"name":"Natural Language Processing Journal","volume":"13 ","pages":"Article 100194"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145571255","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":"Research on the methodology of personalized recommender systems based on multimodal knowledge graphs","authors":"Shaowu Bao ,&nbsp;Jiajia Wang","doi":"10.1016/j.nlp.2025.100193","DOIUrl":"10.1016/j.nlp.2025.100193","url":null,"abstract":"<div><div>The exponential increase in learning materials has occasioned a greater need for personalized learning experiences, yet conventional unimodal recommender systems are not effective in addressing students' diversified demands.In this research, a personalized recommendation system is advocated, backed by a multimodal knowledge graph that consolidates text, image, and video knowledge to improve accuracy, interpretability, and adaptability.The system uses different algorithms for relationship and entity extractions and includes a graph attention module with hierarchical subgraphs to build a semantic network among \"Knowledge Points–Students–Resources.\" A dual-path embedding module that fuses Node2vec for structural semantics with LSTM for learning temporal behavior provides explainable recommendations using path confidence.Experimental results demonstrate that the model's core performance comprehensively outperforms traditional methods and newly introduced comparison models: Entity alignment accuracy (Hits@10=62.7 %) improved by 13.4 % over traditional Node2vec, 6.8 % over KGAT, and 4.2 % over M3KGR; cross-modal similarity (0.76) increased by 11.8 % over traditional Node2vec and 5.6 % over M3KGR. Learning engagement (effective duration 65 %, completion rate 78 %) and knowledge acquisition efficiency (coverage 67 %, cycle reduction 30 %) are significantly optimized, improving by 8.3 %-11.4 % and 8.1 %-20 % over M3KGR respectively; Achieved an explainability score of 4.3 (34.4 %-104.8 % improvement over traditional methods, 22.9 % improvement over KGAT, and 13.2 % improvement over M3KGR), with a response time of 98 ms (40.6 % reduction compared to KGAT and 25.8 % reduction compared to M3KGR).This indicates that multimodal knowledge graph significantly improves recommendation performance through structured semantics and dynamic fusion,providing a new path for personalized education.</div></div>","PeriodicalId":100944,"journal":{"name":"Natural Language Processing Journal","volume":"13 ","pages":"Article 100193"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145519629","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":"Llama3SP: A resource-Efficient large language model for agile story point estimation","authors":"Juan Camilo Sepúlveda Montoya ,&nbsp;Nicole Tatiana Ríos Gómez ,&nbsp;José A. Jaramillo Villegas","doi":"10.1016/j.nlp.2025.100189","DOIUrl":"10.1016/j.nlp.2025.100189","url":null,"abstract":"<div><div>Effort estimation remains a major challenge in Agile software development. Inaccurate story point forecasts can lead to budget overruns, schedule delays, and diminished stakeholder trust. Widely used approaches, such as story point estimation, are helpful for planning but rely heavily on subjective human judgment, making them prone to inconsistency and bias. Prior efforts applying machine learning and natural language processing (e.g. Deep-SE, GPT2SP) to automate story point prediction have achieved only limited success, often suffering from accuracy issues, poor cross-project adaptability, and high computational costs. To address these challenges, we introduce <strong>Llama3SP</strong>, which fine-tunes Meta’s LLaMA 3.2 language model using QLoRA, a resource-efficient adaptation technique. This combination enables training of a high-performance model on standard GPUs without sacrificing prediction quality. Experiments show that Llama3SP provides precise and consistent story point estimates, outperforming or matching previous models like GPT2SP and other comparably sized alternatives, all while operating under significantly lower hardware constraints. These findings highlight how combining advanced NLP models with efficient training techniques can make accurate effort estimation more accessible and practical for agile teams.</div></div>","PeriodicalId":100944,"journal":{"name":"Natural Language Processing Journal","volume":"13 ","pages":"Article 100189"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145519630","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 systematic review of figurative language detection: Methods, challenges, and multilingual perspectives","authors":"Zouheir Banou,&nbsp;Sanaa El Filali,&nbsp;El Habib Benlahmar,&nbsp;Fatima-Zahra Alaoui,&nbsp;Laila El Jiani,&nbsp;Hasnae Sakhi","doi":"10.1016/j.nlp.2025.100192","DOIUrl":"10.1016/j.nlp.2025.100192","url":null,"abstract":"<div><div>Figurative language detection has emerged as a critical task in natural language processing (NLP), enabling machines to comprehend non-literal expressions such as metaphor, irony, and sarcasm. This study presents a systematic literature review with a multilevel analytical framework, examining figurative language across lexical, syntactic, semantic, discourse, and pragmatic levels. We investigate the interplay between feature engineering, model architectures, and annotation strategies across different languages, analyzing datasets, linguistic resources, and evaluation metrics. Special attention is given to morphologically rich and low-resource languages, where deep learning dominates but rule-based and hybrid approaches remain relevant. Our findings indicate that deep learning models–especially transformer-based architectures like BERT and RoBERTa–consistently outperform other approaches, particularly in semantic and discourse-level tasks, due to their ability to capture context-rich and abstract patterns. However, these models often lack interpretability, raising concerns about transparency. Additional challenges include inconsistencies in annotation practices, class imbalance between figurative and literal instances, and limited data coverage for under-resourced languages. The absence of standardized evaluation metrics further complicates cross-study comparison, especially when diverse figurative language styles are involved. By structuring our analysis through linguistic and computational dimensions, this review aims to facilitate the development of more robust, inclusive, and explainable figurative language detection systems.</div></div>","PeriodicalId":100944,"journal":{"name":"Natural Language Processing Journal","volume":"13 ","pages":"Article 100192"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145571256","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}