textshablad

Textshablad is a fictional open-source software framework described here as an example of a modular text-processing system. It is designed to ingest, normalize, label, and analyze large text corpora, with emphasis on transparency and reproducibility.

The architecture comprises a pipeline with stages for ingestion, normalization and tokenization; the SHABLAD module, an acronym for Shaping, Balancing, Labeling, and Adaptation, which assigns labels to text spans according to configurable rules; quality control and provenance tracking; and export options to common data formats.

Textshablad supports pluggable backends for natural language processing tasks such as tokenization, part-of-speech tagging, and named entity recognition, as well as both rule-based and machine learning labeling. It emphasizes streaming processing, modularity, privacy-conscious operation, and auditable labeling decisions.

Potential applications include educational datasets, content moderation trials, linguistic research, and accessibility tooling that require traceable labeling. While fictional, the framework is intended to illustrate how a document-processing system can balance speed, accuracy, and transparency.