prose is a natural language processing library for Go — pure Go, no cgo, no external services. English only.

• Tokenization — words and punctuation, with byte offsets
• Segmentation — sentences, via the Punkt algorithm
• Tagging — part of speech, using the Penn Treebank tag set
• Entities — people, places and organizations
• Readability — Flesch–Kincaid, Gunning fog, SMOG, Coleman–Liau and more
• Case conversion — title and sentence case, AP and Chicago styles

go get github.com/jdkato/prose/v3

package main

import (
    "fmt"
    "log"

    "github.com/jdkato/prose/v3"
)

func main() {
    doc, err := prose.NewDocument("Marie Curie studied in Paris. She won the Nobel Prize twice.")
    if err != nil {
        log.Fatal(err)
    }

    for _, sent := range doc.Sentences() {
        fmt.Println(sent.Text)
    }

    for _, tok := range doc.Tokens() {
        fmt.Println(tok.Text, tok.Tag)
    }

    for _, ent := range doc.Entities() {
        fmt.Println(ent.Text, ent.Label)
        // Marie Curie PERSON
        // Paris GPE
    }
}

Every token, sentence and entity carries a byte offset into the source, and the text at that offset is exactly the text you were given:

src[tok.Start:tok.End()] == tok.Text

That holds for multi-byte input, repeated words and irregular whitespace:

src := "He said “hi there” to me."
doc, _ := prose.NewDocument(src)

for _, tok := range doc.Tokens() {
    fmt.Printf("%-8q [%d:%d] %q\n", tok.Text, tok.Start, tok.End(), tok.In(src))
}

It is also why prose never normalizes text before tokenizing. Replacing curly quotes with straight ones, or ’ with ', changes byte lengths and silently shifts every offset that follows. Normalization that would move bytes is the caller's decision, made before prose sees the text.

prose ships two trained models: a part-of-speech tagger (~3 MB) and an entity recognizer (~4 MB). Each lives in its own package, so the Go linker leaves out whatever you do not import.

Importing the root package links both. If you only need tags, import the tagger directly and the entity model never enters your binary — a 4.5 MB difference:

import "github.com/jdkato/prose/v3/tag"

tagger, err := tag.New()
if err != nil {
    log.Fatal(err)
}

for _, tok := range tagger.Tag([]string{"Kubernetes", "orchestrates", "containers"}) {
    fmt.Println(tok.Text, tok.Tag)
}

Models load once per process and are shared, so building many documents does not repeat the work.

Each component stands alone if you want just that piece:

Skipping entity extraction avoids most of the work:

doc, err := prose.NewDocument(text, prose.WithExtraction(false))

WithTokenization, WithSegmentation, WithTagging and WithExtraction each take a bool. Extraction requires tagging, and tagging requires tokenization, so those are enabled automatically when needed.

Work scales with the length of the input — roughly 1.4 s for a million words — so NewDocumentContext accepts a context.Context:

ctx, cancel := context.WithTimeout(context.Background(), 5*time.Second)
defer cancel()

doc, err := prose.NewDocumentContext(ctx, veryLargeText)

The individual tokenizers and taggers take no context: they work at microsecond scale, where there is nothing worth cancelling.

Tokenizer, Segmenter, Tagger and Extracter are all safe for concurrent use. Their models are immutable once loaded, and per-call scratch space is pooled.

import "github.com/jdkato/prose/v3/summarize"

d := summarize.NewDocument("The quick brown fox jumps over the lazy dog.")
fmt.Println(d.FleschKincaid(), d.GunningFog(), d.SMOG())

import "github.com/jdkato/prose/v3/strcase"

tc := strcase.NewTitleConverter(strcase.APStyle)
fmt.Println(tc.Convert("the quick brown fox"))

MIT. See LICENSE, which also records the upstream work prose derives from.