prose

prose is a natural language processing library (English only, at the moment) in pure Go. It supports tokenization, segmentation, part-of-speech tagging, and named-entity extraction.

You can find a more detailed summary on the library's performance here: Introducing prose v2.0.0: Bringing NLP to Go.

Installation

$ go get github.com/jdkato/prose/v2

Usage

Contents

• Overview
• Tokenizing
• Segmenting
• Tagging
• NER

Overview

package main

import (
    "fmt"
    "log"

    "github.com/jdkato/prose/v2"
)

func main() {
    // Create a new document with the default configuration:
    doc, err := prose.NewDocument("Go is an open-source programming language created at Google.")
    if err != nil {
        log.Fatal(err)
    }

    // Iterate over the doc's tokens:
    for _, tok := range doc.Tokens() {
        fmt.Println(tok.Text, tok.Tag, tok.Label)
        // Go NNP B-GPE
        // is VBZ O
        // an DT O
        // ...
    }

    // Iterate over the doc's named-entities:
    for _, ent := range doc.Entities() {
        fmt.Println(ent.Text, ent.Label)
        // Go GPE
        // Google GPE
    }

    // Iterate over the doc's sentences:
    for _, sent := range doc.Sentences() {
        fmt.Println(sent.Text)
        // Go is an open-source programming language created at Google.
    }
}

The document-creation process adheres to the following sequence of steps:

tokenization -> POS tagging -> NE extraction
            \
             segmentation

Each step may be disabled (assuming later steps aren't required) by passing the appropriate functional option. To disable named-entity extraction, for example, you'd do the following:

doc, err := prose.NewDocument(
        "Go is an open-source programming language created at Google.",
        prose.WithExtraction(false))

Tokenizing

prose includes a tokenizer capable of processing modern text, including the non-word character spans shown below.

Type	Example
Email addresses	[email protected]
Hashtags	#trending
Mentions	@jdkato
URLs	https://github.com/jdkato/prose
Emoticons	:-), >:(, o_0, etc.

package main

import (
    "fmt"
    "log"

    "github.com/jdkato/prose/v2"
)

func main() {
    // Create a new document with the default configuration:
    doc, err := prose.NewDocument("@jdkato, go to http://example.com thanks :).")
    if err != nil {
        log.Fatal(err)
    }

    // Iterate over the doc's tokens:
    for _, tok := range doc.Tokens() {
        fmt.Println(tok.Text, tok.Tag)
        // @jdkato NN
        // , ,
        // go VB
        // to TO
        // http://example.com NN
        // thanks NNS
        // :) SYM
        // . .
    }
}

Segmenting

prose includes one of the most accurate sentence segmenters available, according to the Golden Rules created by the developers of the pragmatic_segmenter.

Name	Language	License	GRS (English)	GRS (Other)	Speed†
Pragmatic Segmenter	Ruby	MIT	98.08% (51/52)	100.00%	3.84 s
prose	Go	MIT	75.00% (39/52)	N/A	0.96 s
TactfulTokenizer	Ruby	GNU GPLv3	65.38% (34/52)	48.57%	46.32 s
OpenNLP	Java	APLv2	59.62% (31/52)	45.71%	1.27 s
Standford CoreNLP	Java	GNU GPLv3	59.62% (31/52)	31.43%	0.92 s
Splitta	Python	APLv2	55.77% (29/52)	37.14%	N/A
Punkt	Python	APLv2	46.15% (24/52)	48.57%	1.79 s
SRX English	Ruby	GNU GPLv3	30.77% (16/52)	28.57%	6.19 s
Scapel	Ruby	GNU GPLv3	28.85% (15/52)	20.00%	0.13 s

† The original tests were performed using a MacBook Pro 3.7 GHz Quad-Core Intel Xeon E5 running 10.9.5, while prose was timed using a MacBook Pro 2.9 GHz Intel Core i7 running 10.13.3.

package main

import (
    "fmt"
    "strings"

    "github.com/jdkato/prose/v2"
)

func main() {
    // Create a new document with the default configuration:
    doc, _ := prose.NewDocument(strings.Join([]string{
        "I can see Mt. Fuji from here.",
        "St. Michael's Church is on 5th st. near the light."}, " "))

    // Iterate over the doc's sentences:
    sents := doc.Sentences()
    fmt.Println(len(sents)) // 2
    for _, sent := range sents {
        fmt.Println(sent.Text)
        // I can see Mt. Fuji from here.
        // St. Michael's Church is on 5th st. near the light.
    }
}

Tagging

prose includes a tagger based on 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:

Library	Accuracy	5-Run Average (sec)
NLTK	0.893	7.224
prose	0.961	2.538

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

The full list of supported POS tags is given below.

TAG	DESCRIPTION
(	left round bracket
)	right round bracket
,	comma
:	colon
.	period
''	closing quotation mark
``	opening quotation mark
#	number sign
$	currency
CC	conjunction, coordinating
CD	cardinal number
DT	determiner
EX	existential there
FW	foreign word
IN	conjunction, subordinating or preposition
JJ	adjective
JJR	adjective, comparative
JJS	adjective, superlative
LS	list item marker
MD	verb, modal auxiliary
NN	noun, singular or mass
NNP	noun, proper singular
NNPS	noun, proper plural
NNS	noun, plural
PDT	predeterminer
POS	possessive ending
PRP	pronoun, personal
PRP$	pronoun, possessive
RB	adverb
RBR	adverb, comparative
RBS	adverb, superlative
RP	adverb, particle
SYM	symbol
TO	infinitival to
UH	interjection
VB	verb, base form
VBD	verb, past tense
VBG	verb, gerund or present participle
VBN	verb, past participle
VBP	verb, non-3rd person singular present
VBZ	verb, 3rd person singular present
WDT	wh-determiner
WP	wh-pronoun, personal
WP$	wh-pronoun, possessive
WRB	wh-adverb

NER

prose v2.0.0 includes a much improved version of v1.0.0's chunk package, which can identify people (PERSON) and geographical/political Entities (GPE) by default.

package main

import (
    "github.com/jdkato/prose/v2"
)

func main() {
    doc, _ := prose.NewDocument("Lebron James plays basketball in Los Angeles.")
    for _, ent := range doc.Entities() {
        fmt.Println(ent.Text, ent.Label)
        // Lebron James PERSON
        // Los Angeles GPE
    }
}

However, in an attempt to make this feature more useful, we've made it straightforward to train your own models for specific use cases. See Prodigy + prose: Radically efficient machine teaching in Go for a tutorial.