prose is a Go library for natural language processing that provides tokenization, part-of-speech tagging, named-entity extraction, and readability metrics without requiring external dependencies or services.
The library solves the problem of processing English text in Go applications by offering pure Go implementations of core NLP tasks. Its approach emphasizes precision and control: every token, sentence, and entity carries byte offsets into the original source text, and the library never normalizes input before processing, leaving that decision to the caller. This design ensures that offsets remain accurate even with multi-byte characters, repeated words, or irregular whitespace. The tool includes trained models for part-of-speech tagging and entity recognition, but allows selective importing so that unused models do not bloat binaries.
Developers should choose this tool when building Go applications that need reliable English text analysis without external service calls or C bindings. It suits projects where binary size matters, since the Go linker excludes unimported models, and where byte-accurate offsets are important for highlighting or editing workflows. The library's modular design means you can import only the components you need—tokenization alone, or tagging without entity extraction—rather than paying the cost of a full pipeline. The tool is particularly valuable in contexts where text normalization must be explicit and controlled by application code rather than hidden inside the library.
Development activity shows consistent maintenance with regular updates addressing edge cases and performance. The project maintains comprehensive test coverage across its modular components and demonstrates attention to concurrent use, with tokenizers, segmenters, taggers, and extractors all designed as safe for parallel processing. The codebase prioritizes backward compatibility while evolving the API, and documentation includes practical examples showing how to skip unnecessary processing stages and handle cancellation for long-running operations.
