构建可复现的古希腊语议会文本解析流水线，解决历史文献处理难题。
A Reproducible Universal Dependencies-Style Pipeline for Katharevousa Greek Parliamentary Text
- 结合OCR修复与大模型辅助标注，实现自动化高质量语料构建
- 产出1697句标准语料，最优模型达0.5162的依存句法准确率
- 开源全流程工具链，适合历史语言与语法研究者使用
古希腊语（Katharevousa）虽在法律、行政与议会档案中具有重要地位，但当前自然语言处理工具对其支持不足。本文提出一个可复现的工作流，用于构建和评估面向该语体的通用依存句法资源，数据源自希腊后独裁时期议会质询文本。流程涵盖：面向OCR的重建、基于模式约束的大模型辅助标注、自动验证、确定性CoNLL-U快照生成、固定划分的评估及模型对比。最终冻结的参考集包含1,697个句子，其中1,357句用于训练，340句作为测试集。我们对比了现成的希腊语与古希腊语解析器、基于特征的解析器、mBERT、XLM-R以及定制Stanza模型。现成系统存在明显语域不匹配，最强外部基线spaCy希腊语仅达0.4183的LAS。最佳结构化模型（XLM-R）取得0.8893的UPOS准确率、0.7250的依存关系F1、0.6098的UAS和0.5162的LAS，较最优外部基线提升0.0980。基于特征的模型在词性标注与关系识别上仍具竞争力，表明在该规模下显式上下文特征依然有效。论文还贡献了一套可审计的方法论，将难处理的历史议会文本转化为可复用的语法分析基础设施。整个流水线——包括代码、标注规范、冻结参考标注、固定训练/测试划分及各模型基准报告——均以开放获取形式发布。
原文摘要 · Abstract (English)
Katharevousa Greek remains poorly served by contemporary NLP pipelines despite its importance for legal, administrative, and parliamentary archives. We present a reproducible workflow for building and evaluating a Universal Dependencies-style parsing resource for Katharevousa parliamentary questions from Greece's early post-junta period. The pipeline links OCR-aware reconstruction, schema-constrained LLM-assisted annotation, automatic validation, deterministic CoNLL-U snapshotting, fixed-split evaluation, and model-family comparison. The frozen automatically validated reference set contains 1{,}697 sentences, split into 1{,}357 training sentences and 340 held-out test sentences. We compare off-the-shelf Greek and Ancient Greek parsers, a feature-based parser, mBERT, XLM-R, and custom Stanza training under the same scoring protocol. Off-the-shelf systems show substantial register mismatch: the strongest external baseline, spaCy Greek, reaches 0.4183 LAS. The best structural parser, an XLM-R model, reaches 0.8893 UPOS accuracy, 0.7250 dependency-relation F1, 0.6098 UAS, and 0.5162 LAS, an absolute LAS gain of 0.0980 over the best external baseline. The feature-based model remains competitive for UPOS and relation labeling, indicating that transparent lexical-context features still matter at this data scale. Beyond scores, the paper contributes an auditable methodology for turning difficult historical parliamentary OCR into reusable syntactic NLP infrastructure. The entire pipeline -- code, schema, frozen reference annotations, fixed train/test split, and per-model benchmark reports -- is released as an open-access companion to this paper.
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