prose

prose is Go library for text (primarily English at the moment) processing that supports tokenization, part-of-speech tagging, named-entity extraction, and more. The library's functionality is split into subpackages designed for modular use. See the documentation for more information.

Install

$ go get github.com/jdkato/prose/...

Usage

Contents

• Tokenizing
• Tagging
• Transforming
• Summarizing
• Chunking
• License

Tokenizing (GoDoc)

Word, sentence, and regexp tokenizers are available. Every tokenizer implements the same interface, which makes it easy to customize tokenization in other parts of the library.

package main

import (
    "fmt"

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

func main() {
    text := "They'll save and invest more."
    tokenizer := tokenize.NewTreebankWordTokenizer()
    for _, word := range tokenizer.Tokenize(text) {
        // [They 'll save and invest more .]
        fmt.Println(word)
    }
}

Tagging (GoDoc)

The tag package includes a port of Textblob's "fast and accurate" POS tagger. Below is a comparison of its performance against NLTK's implementation of the same tagger on the Treebank corpus:

(See scripts/test_model.py for more information.)

package main

import (
    "fmt"

    "github.com/jdkato/prose/tag"
    "github.com/jdkato/prose/tokenize"
)

func main() {
    text := "A fast and accurate part-of-speech tagger for Golang."
    words := tokenize.NewTreebankWordTokenizer().Tokenize(text)

    tagger := tag.NewPerceptronTagger()
    for _, tok := range tagger.Tag(words) {
        fmt.Println(tok.Text, tok.Tag)
    }
}

Transforming (GoDoc)

The tranform package currently only has one function: converting strings to title case. Unlike strings.Title, tranform adheres to common guidelines—including styles for both the AP Stylebook and The Chicago Manual of Style. Additionally, you can easily add your own custom style by defining an IgnoreFunc callback.

Inspiration and test data taken from python-titlecase and to-title-case.

package main

import (
    "fmt"
    "strings"

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

func main() {
    text := "the last of the mohicans"
    tc := transform.NewTitleConverter(transform.APStyle)
    fmt.Println(strings.Title(text))   // The Last Of The Mohicans
    fmt.Println(tc.Title(text)) // The Last of the Mohicans
}

Summarizing (GoDoc)

The summarize package includes functions for computing standard readability and usage statistics. It's among the most accurate implementations available due to its reliance on legitimate tokenizers (whereas others, like readability-score, rely on naive regular expressions).

It also includes a TL;DR algorithm for condensing text into a user-indicated number of paragraphs.

package main

import (
    "fmt"

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

func main() {
    doc := summarize.NewDocument("This is some interesting text.")
    fmt.Println(doc.SMOG(), doc.FleschKincaid())
}

Chunking (GoDoc)

The chunk package implements named-entity extraction using a regular expression indicating what chunks you're looking for and pre-tagged input.

package main

import (
    "fmt"

    "github.com/jdkato/prose/chunk"
    "github.com/jdkato/prose/tag"
    "github.com/jdkato/prose/tokenize"
)

func main() {
    words := tokenize.TextToWords("Go is a open source programming language created at Google.")
    regex := chunk.TreebankNamedEntities

    tagger := tag.NewPerceptronTagger()
    for _, entity := range chunk.Chunk(tagger.Tag(words), regex) {
        fmt.Println(entity) // [Go Google]
    }
}

License

If not otherwise specified (see below), the source files are distributed under MIT License found in the LICENSE file.

Additionally, the following files contain their own license information:

• tag/aptag.go: MIT © Matthew Honnibal.
• tokenize/punkt.go: MIT © Eric Bower.
• tokenize/pragmatic.go: MIT © Kevin S. Dias.