Unlocking Historical Texts: An AI-Powered Pipeline for Katharevousa Greek Parliamentary Records

      In an increasingly digital world, the challenge of preserving and analyzing historical documents remains paramount. For languages with unique historical registers, this challenge is amplified, often leaving vast archives underserved by modern Natural Language Processing (NLP) tools. This is particularly true for Katharevousa Greek, a formal and archaizing register that played a significant role in Greek legal, administrative, and parliamentary discourse. A recent academic paper introduces an innovative, reproducible NLP pipeline designed to unlock the syntactic structure of Katharevousa parliamentary texts, bridging the gap between historical linguistics and cutting-edge AI.

The Linguistic Bottleneck of Historical Texts

      Katharevousa Greek presents a complex linguistic puzzle. It is neither Ancient Greek nor contemporary Demotic Greek but rather a blend of official modern vocabulary, archaizing morphology, and distinct syntactic constructions. This unique linguistic profile means that existing, off-the-shelf NLP parsers—trained on either Ancient or Modern Greek—struggle to accurately interpret Katharevousa. This "register mismatch" leads to poor performance, hindering digital humanities initiatives that aim to extract deeper insights from historical records. Without robust syntactic infrastructure, analyses of argument structure, institutional actors, policy claims, and diachronic language change remain largely out of reach.

      The specific focus of the paper is on Greek parliamentary records from the early post-junta period (1976-1977), a time of democratic transition. These documents, consisting of written parliamentary questions, are historically significant but computationally challenging due to their formal language and the inherent noise introduced by Optical Character Recognition (OCR) processes from digitized archives. This calls for a specialized approach that can handle both the linguistic intricacies and the data quality issues.

An End-to-End Reproducible NLP Pipeline

      The core innovation lies in a comprehensive, reproducible workflow for building and evaluating a Universal Dependencies (UD)-style parsing resource for Katharevousa. Universal Dependencies is a framework that provides a consistent, cross-linguistic representation for tokenization, morphology, and dependency syntax, making it ideal for comparing different linguistic registers and models. The pipeline described in the paper integrates several critical steps:

• OCR-aware reconstruction: Rebuilding text from scanned documents, meticulously handling errors and inconsistencies that arise from optical character recognition.
• Schema-constrained LLM-assisted annotation: Leveraging large language models (LLMs) to aid in annotating linguistic data while ensuring outputs adhere strictly to predefined grammatical schemas and tree constraints. This controlled application of LLMs helps maintain data quality and consistency.
• Automatic validation: Implementing automated checks to ensure the accuracy and integrity of the annotated data.
• Deterministic CoNLL-U snapshotting: Freezing validated annotation batches into a stable, versioned format (CoNLL-U) that serves as a fixed reference set for future experiments.
• Fixed-split evaluation: Ensuring consistent testing by using predefined training and testing datasets.
• Model-family comparison: Benchmarking various NLP models, including off-the-shelf parsers, feature-based models, and advanced transformer models.

      This methodology emphasizes not just the final performance metrics but the entire auditable process, from noisy source material to a clean, inspectable reference set. For enterprises dealing with complex data transformation, such as those handled by custom AI solutions from ARSA Technology, this structured approach is crucial for achieving reliable and scalable outcomes.

From Noisy OCR to Actionable Data

      One of the significant hurdles in historical NLP is the quality of the source material. Scanned documents often contain OCR errors, inconsistent line breaks, historical spelling variations, and missing metadata. The pipeline addresses this by incorporating a dedicated reconstruction workflow:

• Exporting source material from various digital formats (DOCX, XLSX).
• Performing OCR-aware reconstruction of text, which specifically accounts for common OCR artifacts like hyphenation and split words.
• Pre-processing the text to normalize orthographic variations and handle punctuation.
• Finally, deterministically freezing the validated annotation batches into a CoNLL-U snapshot.

      This meticulous approach ensures that the linguistic analysis begins with the cleanest possible data, transforming raw, often degraded historical documents into high-quality, reusable syntactic NLP infrastructure. The resulting reference set, comprising 1,697 sentences split into training and test sets, becomes a stable foundation for developing and evaluating new models.

Benchmarking Performance: Custom Models Lead the Way

      The paper's evaluation phase directly compares various parsing approaches on the newly created Katharevousa dataset. The results underscore the necessity of domain-specific solutions:

• Off-the-shelf parsers: Generic Greek and Ancient Greek parsers performed poorly, with the strongest external baseline (spaCy Greek) achieving a Labeled Attachment Score (LAS) of only 0.4183. This confirms the significant "register mismatch" between Katharevousa and existing resources.
• Advanced Transformer Models: A custom-trained XLM-R model demonstrated substantial improvement, reaching 0.8893 UPOS (Universal Part-of-Speech) accuracy, 0.7250 dependency-relation F1, 0.6098 Unlabeled Attachment Score (UAS), and 0.5162 LAS. This represents an absolute LAS gain of 0.0980 over the best external baseline, highlighting the power of fine-tuning sophisticated models for specific linguistic challenges.
• Feature-based Models: Interestingly, a transparent feature-based model remained competitive for UPOS and relation labeling, suggesting that simple, interpretable lexical-context features still provide significant value, even with modern data scales.

      These findings are crucial for any organization dealing with specialized data, demonstrating that while powerful models like transformers offer high potential, a deep understanding of data characteristics and strategic model selection or customization is key to achieving optimal performance.

The Significance of Reproducible and Auditable NLP

      Beyond the improved parsing scores, a central contribution of this work is the emphasis on reproducibility. The entire pipeline—including code, schemas, frozen reference annotations, fixed train/test splits, and per-model benchmark reports—is released as an open-access companion. This commitment to transparency means every empirical claim is traceable, fostering trust and enabling future research. This practice of open-source release and auditable methodology is especially critical in low-resource historical NLP, where the path to a usable parser often involves numerous experiments and validation decisions.

      The ability to create high-quality, auditable NLP infrastructure for challenging datasets opens new avenues for digital humanities, historical research, and governmental archives. It allows researchers to move beyond superficial text analysis to uncover deeper patterns in historical discourse. This dedication to robust, production-ready AI, applicable across various industries, is a principle also upheld by ARSA Technology, where solutions are engineered to work at scale under real-world constraints.

Partnering for Advanced AI and IoT Solutions

      Just as this research demonstrates the power of tailored AI for complex linguistic challenges, ARSA Technology is committed to delivering practical AI and IoT solutions for global enterprises. From AI video analytics to industrial IoT and custom web platforms, ARSA designs and deploys systems that enhance security, optimize operations, and unlock new business value. This includes navigating challenging data landscapes, ensuring privacy-by-design, and delivering measurable ROI.

      The source paper can be found here: A Reproducible Universal Dependencies-Style Pipeline for Katharevousa Greek Parliamentary Text.

      If your organization faces unique data challenges, whether with historical archives, complex sensor data, or specialized operational requirements, explore ARSA Technology’s comprehensive AI and IoT solutions. Discover how engineering intelligence can transform your operations and request a free consultation today.