Textaleg

Textaleg is a term used in legal technology to describe a framework for converting natural-language legal texts into machine-readable representations that preserve legal structure and meaning. It aims to bridge human-readable statutes and computer-readable data to enable precise search, analysis, and compliance workflows. The concept of textaleg emerged from research in natural language processing and standardization efforts in the 2010s and 2020s, though there is not a single universal specification; multiple projects propose compatible schemas.

Textaleg typically involves three layers: a linguistic layer that extracts sentences, sections, and definitions; a structural layer that maps those elements to a standardized model of statutes, codes, amendments, and cross-references; and a data layer that encodes the model in interoperable formats such as RDF or JSON-LD and uses identifiers like persistent links to sections.

Applications include enhanced search across large corpora of legal texts, automated summarization and clause extraction, change-tracking when laws are amended, and compliance tooling that checks obligations against enterprise policies. Implementations often integrate lemmatization, named-entity recognition for legal terms, and governance rules to avoid misinterpretation.

Limitations include linguistic ambiguity, jurisdictional variation, and the risk of misapplication if machine interpretations are treated as legal advice. Adoption depends on alignment with jurisdiction-specific conventions and the availability of high-quality source materials.

Textaleg remains a developing concept in legal informatics, with ongoing work to standardize vocabularies, mapping strategies, and validation techniques. See also legal tech, legislation analytics, structured data, and ontology in law.