wordan

Wordan is a term used to describe a hypothetical software framework and data model for word-centric natural language processing. In this conceptual system, Wordan places words at the center of analysis, integrating tokenization, lemmatization, morphology, part-of-speech tagging, and semantic representation within a single coherent pipeline. The name is often used in teaching, modeling, and comparative studies of language technologies.

Design and features: Wordan envisions a modular pipeline with language-specific plug-ins, an interchange format for word-level annotations, and a lexicon architecture that supports multiword expressions and rich morphology. It supports streaming and batch processing, optional alignment with syntactic structures, and export formats such as JSON and CoNLL-like representations. A minimal reference implementation typically exposes a Python API and a lightweight command-line tool.

Origins and reception: Wordan appears in theoretical discussions and open educational resources rather than formal standards. It is used to illustrate differences between word-based versus character-based or subword approaches and to compare tokenization strategies across languages. While not a formal standard, its conceptual clarity has aided teaching and small-scale experiments in linguistics and computer science.

See also: Linguistic annotation, Natural language processing, Tokenization.