2026· American Journal of Student Research· 0 citations
TL;DR
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.
Abstract
This narrative review compares contemporary Latin Natural Language Processing (NLP) tools and
their use cases for pedagogical applications. While NLP has advanced significantly for high-resource
modern languages, Latin remains underrepresented due to its complex morphology, flexible word
order, orthographic variation, and limited annotated corpora. To address this gap, this review examines
five prominent Latin NLP systems, LatinBERT, Stanza, LatinCy, LemLat 3.0, and Lamon/LamonPy,
across architecture, training data, task performance, and educational relevance. Drawing on published
evaluation metrics from peer-reviewed sources, including part-of-speech tagging, lemmatization,
dependency parsing, and word sense disambiguation, this study analyzes the strengths and limitations
of each approach. Transformer-based models demonstrate strong contextual understanding and high
accuracy in disambiguation tasks, while rule-based systems offer transparency and reliability for
vocabulary learning. Pipeline models provide comprehensive syntactic analysis but show performance
variability across text genres. The reviewed evidence suggests that no single model performs optimally
across all tasks or corpora, and that performance is strongly influenced by alignment between training
and evaluation data. The review concludes that an integrated, multi-tool approach is most effective
for supporting Latin pedagogy and outlines directions for future research, including standardized
benchmarking and classroom-based evaluation.
Social scientists often machine-translate foreign-language texts into English and apply English-based natural language processing tools without systematically evaluating translation quality or annotation efficiency. To address this problem, this study provides evidence-based guidance for researchers applying English-centric natural language processing to domain-specific foreign-language corpora. We provide and empirically validate a structured framework combining multi-system machine translation evaluation and active learning for domain-specific text classification. Using 11,493 parallel Spanish and Arabic sentences aligned to English, we compare four machine translation systems (Google Translate, Deep, DeepL, OPUS) using SacreBLEU, METEOR, COMET, and BERTScore quality scores. Across languages and metrics, machine translation systems yield statistically comparable performance. We then evaluate eight active learning strategies using ConfliBERT for political conflict classification under a 20% annotation budget, corresponding to 1,155 samples from the training split. Binary classification exceeds F1 = 0.90, while QuadClass multi-class performance peaks around F $1~\approx ~0.75$ . The Ensemble Intersection strategy achieves the highest performance in 53% of tasks and often matches or surpasses full-dataset results using only a fraction of labeled data. These results provide a practical workflow for researchers using English-based natural language processing tools on foreign-language, domain-specific corpora.
Naif Alatrush, Luay Abdeljaber, Javier Osorio et al.· IEEE Access· 0 citations
Building syntactically annotated corpora, such as treebanks, for historical languages is a challenging yet vital task in digital humanities, as it underpins linguistic analysis and facilitates a range of interdisciplinary research. However, the scarcity of annotated data and the need for extensive expertise in historical linguistics make this process particularly demanding. In this study, we explore the potential of cross-lingual natural language processing (NLP) techniques as a semiautomatic solution for treebank construction in low-resource historical languages. We use Middle High German (MHG) as a case study. Leveraging the linguistic continuity and structural similarities between MHG and Modern German (MG), we effectively utilize the extensive MG treebank resources to develop a constituency parsing system tailored for MHG. Specifically, to design a semiautomatic system that integrates automatic annotation with manual validation, we explore two cross-lingual transfer techniques: zero-shot transfer and delexicalization; the latter removes lexical information to focus on syntactic structure. In our experiments, we first train parsers on MG treebanks, and then transfer them to MHG using the two cross-lingual transfer techniques. The delexicalization method achieves a parsing performance of 67.3 per cent in terms of F1-score. This performance significantly surpasses the zero-shot cross-lingual method by a margin of 28.6 percentage points. These investigations validate the effectiveness and feasibility of cross-lingual transfer techniques for historical language treebank construction. This study highlights the potential of NLP tools to streamline the semiautomatic annotation process, reducing the reliance on extensive linguistic expertise and manual effort, and paving the way for broader applications in digital humanities research.
Ercong Nie, Siyao Peng, Helmut Schmid et al.· Digital Scholarship in the H...· 0 citations
The results demonstrate that linear discriminative models and appropriate lexical feature engineering can provide a very accurate and interpretable baseline for the development of natural language processing algorithms for Arabic.
Hamood Mohammed Alrumhi, Muhammad Asshad, Amjed Abbas Ahmed et al.· JOIV: International Journal...· 0 citations
An in-depth evaluation of instruction tuning for Arabic NLP tasks using three prominent LLMs: LLaMA 3.1-8B, AceGPT-v2-8B, and Qwen3-8B shows that instruction tuning consistently improves performance across most tasks, with notable variations in effectiveness across different tasks and prompts.
Maged Saeed Al-shaibani, Zaid Alyafeai, Irfan Ahmad· Language Resources and Evalu...· 0 citations
This paper analyses various recent state-of-the-art variants of large language models (LLMs) and neural machine translation (NMT) for Indian languages in comparison to statistical machine translation (SMT) and tackles key questions, such as idiomatic expressions, morphologically complex grammar or the scarceness of parallel corpora.
Jayanand A. Kamble, S. Jadhav, V. J. Kadam· International Journal of Inf...· 0 citations
Word Sense Disambiguation (WSD) remains a challenge in natural language processing, particularly for morphologically rich and less-resourced languages, e.g., Serbian, where complex inflectional systems intensify lexical ambiguity. Existing WSD approaches often struggle because of scarce annotated corpora, inadequate sense inventories, and linguistically complex languages. This paper presents a semi-automated framework for Serbian WSD resource development that employs Large Language Models (LLMs) and a constrained JSON-formatted output. The approach combines LLM-assisted sense assignment with a Serbian WordNet-based custom inventory, iterative inventory expansion, and expert validation, with the aim of supporting the practical construction and refinement of sense-annotated resources in a low-resource setting.
In WSD experiments, LLMs significantly outperformed the Serbian-specific
TeslaXLM
, the multilingual embedding model
intfloat/multilingual-e5-large
, and the sentence transformer
all-MiniLM-L6-v2
. The expanded sense inventory improved the accuracy, particularly for
GPT-4.1
, which achieved 92.4% for single-word and 95.2% for multi-word spans. These results position LLMs as potentially practical for scalable sense-annotated data creation in less-resourced settings.
The proposed methodology addresses key limitations in existing WSD pipelines for Serbian, and contributes to the broader effort of adapting LLMs for semantic processing in linguistically diverse and data-scarce environments.
Saša Petalinkar, R. Stanković, Milica Ikonić Nešić et al.· Intelligent Data Analysis· 0 citations