2026
EACL
EACL 2026
Armenian AutoEpiDoc: Automated Extraction and Encoding of Armenian Inscriptions into EpiDoc TEI/XML
Abstract
AbstractArmenian epigraphy is extensively documented in printed scholarly corpora, yet lacks machine-readable editions that support interoperability or computational analysis. In this paper, we present Armenian AutoEpiDoc, a system that automatically converts expert-verified Armenian inscription records into EpiDoc-compliant TEI/XML files. Operating on curated and domain-validated data, AutoEpiDoc maps Armenian-specific metadata to EpiDoc structures through rule-based templates and schema-aware validation. The workflow significantly reduces manual encoding effort and provides a scalable path toward producing digital editions and integrating Armenian inscriptions into international epigraphic infrastructures.
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Keyword Pioneer
— epigraphic datum
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Data Science & Analytics, Deep Learning, Interdisciplinary, Machine Learning, Natural Language Processing, Reinforcement Learning, Speech & Audio