Incorporating Linguistic Normalization in Croatian NLP: Evaluating the Impact of Lemmatization on Disinformation Detection Performance

The Effect of Morphological Normalization on Retrieval Performance and Answer Accuracy in Turkish RAG Systems

Retrieval-Augmented Generation (RAG) is a technique that enables large language models to leverage external knowledge sources to improve response quality. This study investigates the effect of morphological normalization on document retrieval and answer quality in Turkish RAG systems. Three indexes were constructed on the RAGTURK dataset: (i) RAW, using surface-form text, (ii) LEMMA, applying full lemmatization, and (iii) HYBRID, combining both representations. Retrieval performance was measured using Recall and MRR, while RAG answer quality was evaluated with ROUGE and BERTScore metrics. Results show that lemmatization does not improve dense retrieval performance for the multilingual embedding model used in this study, and leads to consistent degradation across all query categories. Similarly, RAG answer quality declined under normalization, with RAW indexing achieving the highest performance throughout. These findings suggest that modern multilingual models are capable of implicitly capturing Turkish morphological variation, and that additional normalization is not always necessary.

Arabic Plagiarism Detection Using Word2Vec-Based Semantic Features and Random Forest Classification on the ExAraPlagDet Dataset

The findings underscore the potential of advanced NLP techniques to overcome language-specific challenges, providing a foundation for future research in multilingual plagiarism detection and enhancing the development of tools for other languages facing similar challenges.

Semantic analysis of problems in natural language processing and their mathematical interpretation

Semantic analysis has become a central challenge in natural language processing, driven by exponential growth in digitized textual data and the need for automated content processing across multiple applications including machine translation, text classification, sentiment analysis, and information retrieval. However, while semantic analysis methods are well-developed for resource-rich languages such as English, morphologically complex languages like Uzbek suffer from deficiencies in annotated corpora, lexical-semantic resources, and high-quality vector models – a gap amplified by governmental initiatives in digital economy development and national language technology advancement. This section grounds semantic analysis in the distributional semantics hypothesis principle that words exhibiting similar contexts possess similar meanings – thereby recasting the problem as a geometric challenge within continuous vector spaces. Two principal mathematical strategies are formalized: (1) prediction-based models (word2vec: CBOW/Skip-gram), which optimize context prediction objectives, and (2) count-based models (GloVe), which leverage global co-occurrence statistics through matrix factorization. Both project high-dimensional word co-occurrence relationships into low-dimensional dense vector spaces, enabling semantic analogy representation. For resource-scarce languages like Uzbek, cross-lingual embedding alignment (Procrustes optimization) enables semantic knowledge transfer from resource-rich languages, facilitating shared semantic spaces across the Turkic language family. The section concludes with formal problem specification: given vocabulary V and corpus C, semantic analysis is formalized as (1) a mapping problem preserving distributional properties, (2) an optimization problem minimizing loss through gradient-based methods, and (3) an evaluation problem assessing quality through semantic similarity, analogy, and downstream NLP task performance.

Explainable Subjective Stance Classification with SetFit in Political Discourse

Stance classification in Natural Language Processing (NLP) is not just an academic exercise but a crucial tool for understanding political discourse and the attitudes underlying political statements. This research addresses the challenge of limited annotated datasets in political science by proposing a practical sentence-level dataset sourced from professional politicians for binary subjective stance classification - support or oppose - using bootstrapping in a SetFit model. The study leverages the Sentence Transformers architecture and incorporates traditional linguistic approaches to enhance explainability. We employ corpus linguistics, tailored lexicons, and lexicogrammatical rules to identify key linguistic features such as positive affect, negative affect, pro polarity, con polarity, certainty, emphatics, doubt, hedges. SHAP analysis quantifies the influence of these features on SetFit model decisions. Our findings demonstrate that iterative bootstrapping significantly enhances the efficacy of few-shot learning in subjective stance classification, and we highlight the importance of linguistic features, particularly pro/con polarity and affective expressions. The StanceSentences dataset and our hybrid analytical approach offer a benchmark for future research, emphasizing the need for nuanced, multi-layered analysis in political discourse.

Comparative Evaluation of Latin Natural Language Processing Tools for Pedagogical Applications

A narrative review compares contemporary Latin Natural Language Processing tools and their use cases for pedagogical applications and concludes that an integrated, multi-tool approach is most effective for supporting Latin pedagogy.

@DravidianLangTech 2026: A Comparative Study of TF-IDF SVM and XLM-RoBERTa for Political Multiclass Text Classification.