tekstam

tekstam is a modular, open‑source framework for processing, annotating, and managing large collections of textual data. It is designed to support multilingual corpora, collaborative workflows, and integration with existing data pipelines and content systems. The project emphasizes interoperability, reproducibility, and scalable performance across diverse research and production environments.

The architecture of tekstam centers on a pluggable processing pipeline. Core components cover tokenization, language detection, lemmatization, part-of-speech tagging, named entity recognition, parsing, and sentiment analysis. An annotation layer records decisions with provenance data, enabling traceability and re-use of results. Tekstam supports multiple data models and export formats, including JSON, CSV, and TEI, and offers a RESTful API and command-line tools for automation and integration with other software.

Data management in tekstam follows a structured approach to texts, annotations, and versions. Each text item carries metadata, a version history, and an audit trail showing who made changes and when. The system supports both batch processing and streaming ingestion, making it suitable for digitization projects, ongoing corpora updates, and long-term archival workflows.

Licensing and governance are oriented toward community collaboration. Tekstam is described as being released under a permissive open-source license, with contributions managed through a public repository and standard code-review processes. The project aims to remain language-agnostic and to foster interoperability with other natural language processing tools and data standards.

Typical applications include digital humanities research, linguistic analysis, localization workflows, and scalable text mining. See also natural language processing, corpus linguistics, TEI, and data pipelines.