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/v2Usage
Contents
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
prosewas 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.