⛑️ 😂 📄 ANYKS Pemeriksa ejaan 🙍🏾 😽 🤟🏾

Halo, ini artikel ketiga saya di Habré, sebelumnya saya menulis artikel tentang model bahasa ALM . Sekarang, saya ingin memperkenalkan Anda pada sistem koreksi kesalahan ketik ASC (diimplementasikan atas dasar ALM ).

Ya, ada banyak sekali sistem untuk mengoreksi kesalahan ketik, semuanya memiliki kekuatan dan kelemahan masing-masing, dari sistem terbuka saya dapat memilih salah satu JamSpell yang paling menjanjikan , dan kami akan membandingkannya. Ada juga sistem serupa dari DeepPavlov , yang mungkin dipikirkan banyak orang, tetapi saya tidak pernah berteman dengannya.

Daftar fitur:

Koreksi kesalahan dalam kata-kata dengan perbedaan hingga 4 jarak Levenshtein.
Koreksi kesalahan ketik pada kata-kata (penyisipan, penghapusan, penggantian, penataan ulang) karakter.
fikasi mengingat konteksnya.
Menempatkan kasus huruf pertama dari kata untuk (nama dan gelar yang tepat), dengan mempertimbangkan konteksnya.
Memisahkan kata yang digabungkan menjadi kata-kata terpisah, dengan mempertimbangkan konteksnya.
Melakukan analisis teks tanpa mengoreksi teks asli.
Cari di teks untuk keberadaan (kesalahan, kesalahan ketik, konteks yang salah).

Sistem operasi yang didukung:

MacOS X
FreeBSD
Linux

Sistem ini ditulis dalam C ++ 11, ada port untuk Python3

Kamus siap

Nama	Ukuran (GB)	RAM (GB)	Ukuran N-gram	Bahasa
wittenbell-3-big.asc	1.97	15.6	3	RU
wittenbell-3-middle.asc	1.24	9.7	3	RU
mkneserney-3-middle.asc	1.33	9.7	3	RU
wittenbell-3-single.asc	0.772	5.14	3	RU
wittenbell-5-single.asc	1.37	10.7	lima	RU

Menguji

Data dari kompetisi "koreksi kesalahan ketik" Dialog21 2016 digunakan untuk menguji sistem . Kamus biner terlatih digunakan untuk pengujian: wittenbell-3-middle.asc

Tes dilakukan	Presisi	Penarikan	Ukur
Mode koreksi salah ketik	76.97	62.71	69.11
Mode koreksi kesalahan	73.72	60.53	66.48

Saya pikir tidak perlu menambahkan data lain, jika diinginkan, semua orang dapat mengulangi tes, saya lampirkan semua materi yang digunakan dalam pengujian di bawah ini.

Bahan yang digunakan dalam pengujian

test.txt - Teks untuk diuji
correct.txt - Teks dari varian yang benar
evalu.py - skrip Python3 untuk menghitung hasil koreksi

Sekarang, menarik untuk membandingkan kerja sistem untuk mengoreksi kesalahan ketik itu sendiri dalam kondisi yang sama, kami akan melatih dua kesalahan ketik yang berbeda pada data teks yang sama dan melakukan tes.

Sebagai perbandingan, mari kita ambil sistem koreksi kesalahan ketik yang saya sebutkan di atas, JamSpell .

ASC vs JamSpell

Instalasi

ASC

$ git clone --recursive https://github.com/anyks/asc.git
$ cd ./asc

$ mkdir ./build
$ cd ./build

$ cmake ..
$ make

JamSpell

$ git clone https://github.com/bakwc/JamSpell.git
$ cd ./JamSpell

$ mkdir ./build
$ cd ./build

$ cmake ..
$ make

Latihan

ASC

train.json

{
  "ext": "txt",
  "size": 3,
  "alter": {"":""},
  "debug": 1,
  "threads": 0,
  "method": "train",
  "allow-unk": true,
  "reset-unk": true,
  "confidence": true,
  "interpolate": true,
  "mixed-dicts": true,
  "only-token-words": true,
  "locale": "en_US.UTF-8",
  "smoothing": "wittenbell",
  "pilots": ["","","","","","","","","","","a","i","o","e","g"],
  "corpus": "./texts/correct.txt",
  "w-bin": "./dictionary/3-middle.asc",
  "w-vocab": "./train/lm.vocab",
  "w-arpa": "./train/lm.arpa",
  "mix-restwords": "./similars/letters.txt",
  "alphabet": "abcdefghijklmnopqrstuvwxyz",
  "bin-code": "ru",
  "bin-name": "Russian",
  "bin-author": "You name",
  "bin-copyright": "You company LLC",
  "bin-contacts": "site: https://example.com, e-mail: info@example.com",
  "bin-lictype": "MIT",
  "bin-lictext": "... License text ...",
  "embedding-size": 28,
  "embedding": {
      "": 0, "": 1, "": 2, "": 3, "": 4, "": 5,
      "": 5, "": 6, "": 7, "": 8, "": 8, "": 9,
      "": 10, "": 11, "": 12, "": 0, "": 13, "": 14,
      "": 15, "": 16, "": 17, "": 18, "": 19, "": 20,
      "": 21, "": 21, "": 21, "": 22, "": 23, "": 22,
      "": 5, "": 24, "": 25, "<": 26, ">": 26, "~": 26,
      "-": 26, "+": 26, "=": 26, "*": 26, "/": 26, ":": 26,
      "%": 26, "|": 26, "^": 26, "&": 26, "#": 26, "'": 26,
      "\\": 26, "0": 27, "1": 27, "2": 27, "3": 27, "4": 27,
      "5": 27, "6": 27, "7": 27, "8": 27, "9": 27, "a": 0,
      "b": 2, "c": 15, "d": 4, "e": 5, "f": 18, "g": 3,
      "h": 12, "i": 8, "j": 6, "k": 9, "l": 10, "m": 11,
      "n": 12, "o": 0, "p": 14, "q": 13, "r": 14, "s": 15,
      "t": 16, "u": 24, "v": 21, "w": 22, "x": 19, "y": 17, "z": 7
  }
}

$ ./asc -r-json ./train.json

Python3

import asc

asc.setSize(3)
asc.setAlmV2()
asc.setThreads(0)
asc.setLocale("en_US.UTF-8")

asc.setOption(asc.options_t.uppers)
asc.setOption(asc.options_t.allowUnk)
asc.setOption(asc.options_t.resetUnk)
asc.setOption(asc.options_t.mixDicts)
asc.setOption(asc.options_t.tokenWords)
asc.setOption(asc.options_t.confidence)
asc.setOption(asc.options_t.interpolate)

asc.setAlphabet("abcdefghijklmnopqrstuvwxyz")

asc.setPilots(["","","","","","","","","","","a","i","o","e","g"])
asc.setSubstitutes({'p':'','c':'','o':'','t':'','k':'','e':'','a':'','h':'','x':'','b':'','m':''})

def statusArpa1(status):
    print("Build arpa", status)

def statusArpa2(status):
    print("Write arpa", status)

def statusVocab(status):
    print("Write vocab", status)

def statusIndex(text, status):
    print(text, status)

def status(text, status):
    print(text, status)

asc.collectCorpus("./texts/correct.txt", asc.smoothing_t.wittenBell, 0.0, False, False, status)

asc.buildArpa(statusArpa1)

asc.writeArpa("./train/lm.arpa", statusArpa2)

asc.writeVocab("./train/lm.vocab", statusVocab)

asc.setCode("RU")
asc.setLictype("MIT")
asc.setName("Russian")
asc.setAuthor("You name")
asc.setCopyright("You company LLC")
asc.setLictext("... License text ...")
asc.setContacts("site: https://example.com, e-mail: info@example.com")

asc.setEmbedding({
     "": 0, "": 1, "": 2, "": 3, "": 4, "": 5,
     "": 5, "": 6, "": 7, "": 8, "": 8, "": 9,
     "": 10, "": 11, "": 12, "": 0, "": 13, "": 14,
     "": 15, "": 16, "": 17, "": 18, "": 19, "": 20,
     "": 21, "": 21, "": 21, "": 22, "": 23, "": 22,
     "": 5, "": 24, "": 25, "<": 26, ">": 26, "~": 26,
     "-": 26, "+": 26, "=": 26, "*": 26, "/": 26, ":": 26,
     "%": 26, "|": 26, "^": 26, "&": 26, "#": 26, "'": 26,
     "\\": 26, "0": 27, "1": 27, "2": 27, "3": 27, "4": 27,
     "5": 27, "6": 27, "7": 27, "8": 27, "9": 27, "a": 0,
     "b": 2, "c": 15, "d": 4, "e": 5, "f": 18, "g": 3,
     "h": 12, "i": 8, "j": 6, "k": 9, "l": 10, "m": 11,
     "n": 12, "o": 0, "p": 14, "q": 13, "r": 14, "s": 15,
     "t": 16, "u": 24, "v": 21, "w": 22, "x": 19, "y": 17, "z": 7
}, 28)

asc.saveIndex("./dictionary/3-middle.asc", "", 128, statusIndex)

JamSpell

$ ./main/jamspell train ../test_data/alphabet_ru.txt ../test_data/correct.txt ./model.bin

Menguji

ASC

spell.json

{
    "debug": 1,
    "threads": 0,
    "method": "spell",
    "spell-verbose": true,
    "confidence": true,
    "mixed-dicts": true,
    "asc-split": true,
    "asc-alter": true,
    "asc-esplit": true,
    "asc-rsplit": true,
    "asc-uppers": true,
    "asc-hyphen": true,
    "asc-wordrep": true,
    "r-text": "./texts/test.txt",
    "w-text": "./texts/output.txt",
    "r-bin": "./dictionary/3-middle.asc"
}

$ ./asc -r-json ./spell.json

Python3

import asc

asc.setAlmV2()
asc.setThreads(0)

asc.setOption(asc.options_t.uppers)
asc.setOption(asc.options_t.ascSplit)
asc.setOption(asc.options_t.ascAlter)
asc.setOption(asc.options_t.ascESplit)
asc.setOption(asc.options_t.ascRSplit)
asc.setOption(asc.options_t.ascUppers)
asc.setOption(asc.options_t.ascHyphen)
asc.setOption(asc.options_t.ascWordRep)
asc.setOption(asc.options_t.mixDicts)
asc.setOption(asc.options_t.confidence)

def status(text, status):
    print(text, status)

asc.loadIndex("./dictionary/3-middle.asc", "", status)

f1 = open('./texts/test.txt')
f2 = open('./texts/output.txt', 'w')

for line in f1.readlines():
    res = asc.spell(line)
    f2.write("%s\n" % res[0])

f2.close()
f1.close()

JamSpell

- Python , C++

#include <fstream>
#include <iostream>
#include <jamspell/spell_corrector.hpp>

//   BOOST
#ifdef USE_BOOST_CONVERT
	#include <boost/locale/encoding_utf.hpp>
//     
#else
	#include <codecvt>
#endif

using namespace std;

/**
 * convert    utf-8  
 * @param  str  utf-8  
 * @return      
 */
const string convert(const wstring & str){
	//   
	string result = "";
	//   
	if(!str.empty()){
//   BOOST
#ifdef USE_BOOST_CONVERT
		//  
		using boost::locale::conv::utf_to_utf;
		//    utf-8 
		result = utf_to_utf <char> (str.c_str(), str.c_str() + str.size());
//     
#else
		//     UTF-8
		using convert_type = codecvt_utf8 <wchar_t, 0x10ffff, little_endian>;
		//  
		wstring_convert <convert_type, wchar_t> conv;
		// wstring_convert <codecvt_utf8 <wchar_t>> conv;
		//    utf-8 
		result = conv.to_bytes(str);
#endif
	}
	//  
	return result;
}

/**
 * convert      utf-8
 * @param  str   
 * @return       utf-8
 */
const wstring convert(const string & str){
	//   
	wstring result = L"";
	//   
	if(!str.empty()){
//   BOOST
#ifdef USE_BOOST_CONVERT
		//  
		using boost::locale::conv::utf_to_utf;
		//    utf-8 
		result = utf_to_utf <wchar_t> (str.c_str(), str.c_str() + str.size());
//     
#else
		//  
		// wstring_convert <codecvt_utf8 <wchar_t>> conv;
		wstring_convert <codecvt_utf8_utf16 <wchar_t, 0x10ffff, little_endian>> conv;
		//    utf-8 
		result = conv.from_bytes(str);
#endif
	}
	//  
	return result;
}

/**
 * safeGetline     
 * @param  is  
 * @param  t     
 * @return     
 */
istream & safeGetline(istream & is, string & t){
	//  
	t.clear();

	istream::sentry se(is, true);
	streambuf * sb = is.rdbuf();

	for(;;){
		int c = sb->sbumpc();
		switch(c){
 			case '\n': return is;
			case '\r':
				if(sb->sgetc() == '\n') sb->sbumpc();
				return is;
			case streambuf::traits_type::eof():
				if(t.empty()) is.setstate(ios::eofbit);
				return is;
			default: t += (char) c;
		}
	}
}

/**
* main   
*/
int main(){
	//  
	NJamSpell::TSpellCorrector corrector;
	//   
	corrector.LoadLangModel("model.bin");
	//    
	ifstream file1("./test_data/test.txt", ios::in);
	//   
	if(file1.is_open()){
		//    
		string line = "", res = "";
		//    
		ofstream file2("./test_data/output.txt", ios::out);
		//   
		if(file2.is_open()){
			//       
			while(file1.good()){
				//    
				safeGetline(file1, line);
				//   ,  
				if(!line.empty()){
					//   
					res = convert(corrector.FixFragment(convert(line)));
					//   ,    
					if(!res.empty()){
						//   
						res.append("\n");
						//    
						file2.write(res.c_str(), res.size());
					}
				}
			}
			//  
			file2.close();
		}
		//  
		file1.close();
	}
    return 0;
}

$ g++ -std=c++11 -I../JamSpell -L./build/jamspell -L./build/contrib/cityhash -L./build/contrib/phf -ljamspell_lib -lcityhash -lphf ./test.cpp -o ./bin/test

$ ./bin/test

hasil

Mendapatkan hasil

$ python3 evaluate.py ./texts/test.txt ./texts/correct.txt ./texts/output.txt

ASC

Presisi	Penarikan	Ukur
92.13	82.51	87.05

JamSpell

Presisi	Penarikan	Ukur
77.87	63.36	69.87

Salah satu fitur utama ASC adalah belajar dari data kotor. Praktis tidak mungkin menemukan corpora teks tanpa kesalahan dan kesalahan ketik dalam akses terbuka. Hidup tidak cukup untuk memperbaiki terabyte data dengan tangan, tetapi Anda harus mengatasinya.

Prinsip pengajaran yang saya tawarkan

Menyusun model bahasa menggunakan data kotor
Kami menghapus semua kata langka dan N-gram dalam model bahasa yang dirakit
Kami menambahkan satu kata untuk pengoperasian yang lebih benar dari sistem koreksi kesalahan ketik.
Menyusun kamus biner

Mari kita mulai

Misalkan kita memiliki beberapa korpus dari subjek yang berbeda, lebih logis untuk melatihnya secara terpisah, kemudian menggabungkannya.

Merakit sasis menggunakan ALM

collect.json

{
	"size": 3,
	"debug": 1,
	"threads": 0,
	"ext": "txt",
	"method": "train",
	"allow-unk": true,
	"mixed-dicts": true,
	"only-token-words": true,
	"smoothing": "wittenbell",
	"locale": "en_US.UTF-8",
	"w-abbr": "./output/alm.abbr",
	"w-map": "./output/alm.map",
	"w-vocab": "./output/alm.vocab",
	"w-words": "./output/words.txt",
	"corpus": "./texts/corpus",
	"abbrs": "./abbrs/abbrs.txt",
	"goodwords": "./texts/whitelist/words.txt",
	"badwords": "./texts/blacklist/garbage.txt",
	"mix-restwords": "./texts/similars/letters.txt",
	"alphabet": "abcdefghijklmnopqrstuvwxyz"
}

$ ./alm -r-json ./collect.json

size — N- 3
debug —
threads —
ext —
allow-unk — 〈unk〉
mixed-dicts —
only-token-words — N- —
smoothing — wittenbell ( , - )
locale — ( )
w-abbr —
w-map —
w-vocab —
w-words — ( )
corpus —
abbrs — , , (, , ...)
goodwords —
badwords —
mix-restwords —
alphabet — ( )

Python

import alm

#   N-  3
alm.setSize(3)
#      
alm.setThreads(0)
#    (  )
alm.setLocale("en_US.UTF-8")
#      (        )
alm.setAlphabet("abcdefghijklmnopqrstuvwxyz")
#     
alm.setSubstitutes({'p':'','c':'','o':'','t':'','k':'','e':'','a':'','h':'','x':'','b':'','m':''})

#    <unk>   
alm.setOption(alm.options_t.allowUnk)
#         
alm.setOption(alm.options_t.mixDicts)
#    N- —       
alm.setOption(alm.options_t.tokenWords)

#    wittenbell (     ,  -    )
alm.init(alm.smoothing_t.wittenBell)

#   ,  ,   (, ,  ...)
f = open('./abbrs/abbrs.txt')
for abbr in f.readlines():
    abbr = abbr.replace("\n", "")
    alm.addAbbr(abbr)
f.close()

#      
f = open('./texts/whitelist/words.txt')
for word in f.readlines():
    word = word.replace("\n", "")
    alm.addGoodword(word)
f.close()

#      
f = open('./texts/blacklist/garbage.txt')
for word in f.readlines():
    word = word.replace("\n", "")
    alm.addBadword(word)
f.close()

def status(text, status):
    print(text, status)

def statusWords(status):
    print("Write words", status)

def statusVocab(status):
    print("Write vocab", status)

def statusMap(status):
    print("Write map", status)

def statusSuffix(status):
    print("Write suffix", status)

#    
alm.collectCorpus("./texts/corpus", status)
#      
alm.writeWords("./output/words.txt", statusWords)
#   
alm.writeVocab("./output/alm.vocab", statusVocab)
#    
alm.writeMap("./output/alm.map", statusMap)
#      
alm.writeSuffix("./output/alm.abbr", statusSuffix)

Pemangkasan Hull Rakitan dengan ALM

prune.json

{
    "size": 3,
    "debug": 1,
    "allow-unk": true,
    "method": "vprune",
    "vprune-wltf": -15.0,
    "locale": "en_US.UTF-8",
    "smoothing": "wittenbell",
    "r-map": "./corpus1/alm.map",
    "r-vocab": "./corpus1/alm.vocab",
    "w-map": "./output/alm.map",
    "w-vocab": "./output/alm.vocab",
    "goodwords": "./texts/whitelist/words.txt",
    "badwords": "./texts/blacklist/garbage.txt",
    "alphabet": "abcdefghijklmnopqrstuvwxyz"
}

$ ./alm -r-json ./prune.json

size — N- 3
debug —
allow-unk — 〈unk〉
vprune-wltf — - (, — )
locale — ( )
smoothing — wittenbell ( , - )
r-map —
r-vocab —
w-map —
w-vocab —
goodwords —
badwords —
alphabet — ( )

Python

import alm

#   N-  3
alm.setSize(3)
#      
alm.setThreads(0)
#    (  )
alm.setLocale("en_US.UTF-8")
#      (        )
alm.setAlphabet("abcdefghijklmnopqrstuvwxyz")

#    <unk>   
alm.setOption(alm.options_t.allowUnk)

#    wittenbell (     ,  -    )
alm.init(alm.smoothing_t.wittenBell)

#      
f = open('./texts/whitelist/words.txt')
for word in f.readlines():
    word = word.replace("\n", "")
    alm.addGoodword(word)
f.close()

#      
f = open('./texts/blacklist/garbage.txt')
for word in f.readlines():
    word = word.replace("\n", "")
    alm.addBadword(word)
f.close()

def statusPrune(status):
    print("Prune data", status)

def statusReadVocab(text, status):
    print("Read vocab", text, status)

def statusWriteVocab(status):
    print("Write vocab", status)

def statusReadMap(text, status):
    print("Read map", text, status)

def statusWriteMap(status):
    print("Write map", status)

#  
alm.readVocab("./corpus1/alm.vocab", statusReadVocab)
#    
alm.readMap("./corpus1/alm.map", statusReadMap)
#   
alm.pruneVocab(-15.0, 0, 0, statusPrune)
#   
alm.writeVocab("./output/alm.vocab", statusWriteVocab)
#    
alm.writeMap("./output/alm.map", statusWriteMap)

Menggabungkan data yang dikumpulkan dengan ALM

merge.json

{
    "size": 3,
    "debug": 1,
    "allow-unk": true,
    "method": "merge",
    "mixed-dicts": "true",
    "locale": "en_US.UTF-8",
    "smoothing": "wittenbell",
    "r-words": "./texts/words",
    "r-map": "./corpus1",
    "r-vocab": "./corpus1",
    "w-map": "./output/alm.map",
    "w-vocab": "./output/alm.vocab",
    "goodwords": "./texts/whitelist/words.txt",
    "badwords": "./texts/blacklist/garbage.txt",
    "mix-restwords": "./texts/similars/letters.txt",
    "alphabet": "abcdefghijklmnopqrstuvwxyz"
}

$ ./alm -r-json ./merge.json

size — N- 3
debug —
allow-unk — 〈unk〉
mixed-dicts —
locale — ( )
smoothing — wittenbell ( , - )
r-words —
r-map — ,
r-vocab — ,
w-map —
w-vocab —
goodwords —
badwords —
alphabet — ( )

Python

import alm

#   N-  3
alm.setSize(3)
#      
alm.setThreads(0)
#    (  )
alm.setLocale("en_US.UTF-8")
#      (        )
alm.setAlphabet("abcdefghijklmnopqrstuvwxyz")
#     
alm.setSubstitutes({'p':'','c':'','o':'','t':'','k':'','e':'','a':'','h':'','x':'','b':'','m':''})

#    <unk>   
alm.setOption(alm.options_t.allowUnk)
#         
alm.setOption(alm.options_t.mixDicts)

#    wittenbell (     ,  -    )
alm.init(alm.smoothing_t.wittenBell)

#      
f = open('./texts/whitelist/words.txt')
for word in f.readlines():
    word = word.replace("\n", "")
    alm.addGoodword(word)
f.close()

#      
f = open('./texts/blacklist/garbage.txt')
for word in f.readlines():
    word = word.replace("\n", "")
    alm.addBadword(word)
f.close()

#         
f = open('./texts/words.txt')
for word in f.readlines():
    word = word.replace("\n", "")
    alm.addWord(word)
f.close()

def statusReadVocab(text, status):
    print("Read vocab", text, status)

def statusWriteVocab(status):
    print("Write vocab", status)

def statusReadMap(text, status):
    print("Read map", text, status)

def statusWriteMap(status):
    print("Write map", status)

#   
alm.readVocab("./corpus1", statusReadVocab)
#    
alm.readMap("./corpus1", statusReadMap)
#   
alm.writeVocab("./output/alm.vocab", statusWriteVocab)
#    
alm.writeMap("./output/alm.map", statusWriteMap)

Mempelajari model bahasa dengan ALM

train.json

{
    "size": 3,
    "debug": 1,
    "allow-unk": true,
    "reset-unk": true,
    "interpolate": true,
    "method": "train",
    "locale": "en_US.UTF-8",
    "smoothing": "wittenbell",
    "r-map": "./output/alm.map",
    "r-vocab": "./output/alm.vocab",
    "w-arpa": "./output/alm.arpa",
    "w-words": "./output/words.txt",
    "alphabet": "abcdefghijklmnopqrstuvwxyz"
}

$ ./alm -r-json ./train.json

size — N- 3
debug —
allow-unk — 〈unk〉
reset-unk — , 〈unk〉
interpolate —
locale — ( )
smoothing — wittenbell
r-map — ,
r-vocab — ,
w-arpa — ARPA,
w-words — , ( )
alphabet — ( )

Python

import alm

#   N-  3
alm.setSize(3)
#      
alm.setThreads(0)
#    (  )
alm.setLocale("en_US.UTF-8")
#      (        )
alm.setAlphabet("abcdefghijklmnopqrstuvwxyz")
#     
alm.setSubstitutes({'p':'','c':'','o':'','t':'','k':'','e':'','a':'','h':'','x':'','b':'','m':''})

#    <unk>   
alm.setOption(alm.options_t.allowUnk)
#      <unk>   
alm.setOption(alm.options_t.resetUnk)
#         
alm.setOption(alm.options_t.mixDicts)
#     
alm.setOption(alm.options_t.interpolate)

#    wittenbell (     ,  -    )
alm.init(alm.smoothing_t.wittenBell)

def statusReadVocab(text, status):
    print("Read vocab", text, status)

def statusReadMap(text, status):
    print("Read map", text, status)

def statusBuildArpa(status):
    print("Build ARPA", status)

def statusWriteMap(status):
    print("Write map", status)

def statusWriteArpa(status):
    print("Write ARPA", status)

def statusWords(status):
    print("Write words", status)

#   
alm.readVocab("./output/alm.vocab", statusReadVocab)
#    
alm.readMap("./output/alm.map", statusReadMap)

#     
alm.buildArpa(statusBuildArpa)

#       ARPA
alm.writeArpa("./output/alm.arpa", statusWriteArpa)

#   
alm.writeWords("./output/words.txt", statusWords)

Pelatihan ASC pemeriksa ejaan

train.json

{
	"size": 3,
	"debug": 1,
	"threads": 0,
	"confidence": true,
	"mixed-dicts": true,
	"method": "train",
	"alter": {"":""},
	"locale": "en_US.UTF-8",
	"smoothing": "wittenbell",
	"pilots": ["","","","","","","","","","","a","i","o","e","g"],
	"w-bin": "./dictionary/3-single.asc",
	"r-abbr": "./output/alm.abbr",
	"r-vocab": "./output/alm.vocab",
	"r-arpa": "./output/alm.arpa",
	"abbrs": "./texts/abbrs/abbrs.txt",
	"goodwords": "./texts/whitelist/words.txt",
	"badwords": "./texts/blacklist/garbage.txt",
	"alters": "./texts/alters/yoficator.txt",
	"upwords": "./texts/words/upp",
	"mix-restwords": "./texts/similars/letters.txt",
	"alphabet": "abcdefghijklmnopqrstuvwxyz",
	"bin-code": "ru",
	"bin-name": "Russian",
	"bin-author": "You name",
	"bin-copyright": "You company LLC",
	"bin-contacts": "site: https://example.com, e-mail: info@example.com",
	"bin-lictype": "MIT",
	"bin-lictext": "... License text ...",
	"embedding-size": 28,
	"embedding": {
	    "": 0, "": 1, "": 2, "": 3, "": 4, "": 5,
	    "": 5, "": 6, "": 7, "": 8, "": 8, "": 9,
	    "": 10, "": 11, "": 12, "": 0, "": 13, "": 14,
	    "": 15, "": 16, "": 17, "": 18, "": 19, "": 20,
	    "": 21, "": 21, "": 21, "": 22, "": 23, "": 22,
	    "": 5, "": 24, "": 25, "<": 26, ">": 26, "~": 26,
	    "-": 26, "+": 26, "=": 26, "*": 26, "/": 26, ":": 26,
	    "%": 26, "|": 26, "^": 26, "&": 26, "#": 26, "'": 26,
	    "\\": 26, "0": 27, "1": 27, "2": 27, "3": 27, "4": 27,
	    "5": 27, "6": 27, "7": 27, "8": 27, "9": 27, "a": 0,
	    "b": 2, "c": 15, "d": 4, "e": 5, "f": 18, "g": 3,
	    "h": 12, "i": 8, "j": 6, "k": 9, "l": 10, "m": 11,
	    "n": 12, "o": 0, "p": 14, "q": 13, "r": 14, "s": 15,
	    "t": 16, "u": 24, "v": 21, "w": 22, "x": 19, "y": 17, "z": 7
	}
}

$ ./asc -r-json ./train.json

size — N- 3
debug —
threads —
confidence — ARPA - ,
mixed-dicts —
alter — ( , , — «»)
locale — ( )
smoothing — wittenbell ( , - )
pilots — ( )
w-bin —
r-abbr — ,
r-vocab — ,
r-arpa — ARPA,
abbrs — , , (, , ...)
goodwords —
badwords —
alters — , ( )
upwords — , (, , ...)
mix-restwords —
alphabet — ( )
bin-code —
bin-name —
bin-author —
bin-copyright —
bin-contacts —
bin-lictype —
bin-lictext —
embedding-size —
embedding — ( , )

Python

import asc

#   N-  3
asc.setSize(3)
#      
asc.setThreads(0)
#    (  )
asc.setLocale("en_US.UTF-8")

#        
asc.setOption(asc.options_t.uppers)
#    <unk>   
asc.setOption(asc.options_t.allowUnk)
#      <unk>   
asc.setOption(asc.options_t.resetUnk)
#         
asc.setOption(asc.options_t.mixDicts)
#     ARPA -  ,  
asc.setOption(asc.options_t.confidence)

#      (        )
asc.setAlphabet("abcdefghijklmnopqrstuvwxyz")
#     (     )
asc.setPilots(["","","","","","","","","","","a","i","o","e","g"])
#     
asc.setSubstitutes({'p':'','c':'','o':'','t':'','k':'','e':'','a':'','h':'','x':'','b':'','m':''})

#       
f = open('./texts/whitelist/words.txt')
for word in f.readlines():
    word = word.replace("\n", "")
    asc.addGoodword(word)
f.close()

#       
f = open('./texts/blacklist/garbage.txt')
for word in f.readlines():
    word = word.replace("\n", "")
    asc.addBadword(word)
f.close()

#     
f = open('./output/alm.abbr')
for word in f.readlines():
    word = word.replace("\n", "")
    asc.addSuffix(word)
f.close()

#    ,   (, ,  ...)
f = open('./texts/abbrs/abbrs.txt')
for abbr in f.readlines():
    abbr = abbr.replace("\n", "")
    asc.addAbbr(abbr)
f.close()

#     ,       (, , ...)
f = open('./texts/words/upp/words.txt')
for word in f.readlines():
    word = word.replace("\n", "")
    asc.addUWord(word)
f.close()

#   
asc.addAlt("", "")

#       ,     (        )
f = open('./texts/alters/yoficator.txt')
for words in f.readlines():
    words = words.replace("\n", "")
    words = words.split('\t')
    asc.addAlt(words[0], words[1])
f.close()

def statusIndex(text, status):
    print(text, status)

def statusBuildIndex(status):
    print("Build index", status)

def statusArpa(status):
    print("Read arpa", status)

def statusVocab(status):
    print("Read vocab", status)

#        ARPA
asc.readArpa("./output/alm.arpa", statusArpa)
#   
asc.readVocab("./output/alm.vocab", statusVocab)

#     
asc.setCode("RU")
#    
asc.setLictype("MIT")
#   
asc.setName("Russian")
#    
asc.setAuthor("You name")
#   
asc.setCopyright("You company LLC")
#    
asc.setLictext("... License text ...")
#     
asc.setContacts("site: https://example.com, e-mail: info@example.com")

#      ( ,     )
asc.setEmbedding({
    "": 0, "": 1, "": 2, "": 3, "": 4, "": 5,
    "": 5, "": 6, "": 7, "": 8, "": 8, "": 9,
    "": 10, "": 11, "": 12, "": 0, "": 13, "": 14,
    "": 15, "": 16, "": 17, "": 18, "": 19, "": 20,
    "": 21, "": 21, "": 21, "": 22, "": 23, "": 22,
    "": 5, "": 24, "": 25, "<": 26, ">": 26, "~": 26,
    "-": 26, "+": 26, "=": 26, "*": 26, "/": 26, ":": 26,
    "%": 26, "|": 26, "^": 26, "&": 26, "#": 26, "'": 26,
    "\\": 26, "0": 27, "1": 27, "2": 27, "3": 27, "4": 27,
    "5": 27, "6": 27, "7": 27, "8": 27, "9": 27, "a": 0,
    "b": 2, "c": 15, "d": 4, "e": 5, "f": 18, "g": 3,
    "h": 12, "i": 8, "j": 6, "k": 9, "l": 10, "m": 11,
    "n": 12, "o": 0, "p": 14, "q": 13, "r": 14, "s": 15,
    "t": 16, "u": 24, "v": 21, "w": 22, "x": 19, "y": 17, "z": 7
}, 28)

#     
asc.buildIndex(statusBuildIndex)

#     
asc.saveIndex("./dictionary/3-middle.asc", "", 128, statusIndex)

Saya memahami bahwa tidak setiap orang dapat melatih kosakata biner mereka sendiri; ini membutuhkan corpora teks dan sumber daya komputasi yang signifikan. Oleh karena itu, ASC hanya mampu bekerja dengan satu file ARPA sebagai kamus utama.

Contoh pekerjaan

spell.json

{
    "ad": 13,
    "cw": 38120,
    "debug": 1,
    "threads": 0,
    "method": "spell",
    "alter": {"":""},
    "asc-split": true,
    "asc-alter": true,
    "confidence": true,
    "asc-esplit": true,
    "asc-rsplit": true,
    "asc-uppers": true,
    "asc-hyphen": true,
    "mixed-dicts": true,
    "asc-wordrep": true,
    "spell-verbose": true,
    "r-text": "./texts/test.txt",
    "w-text": "./texts/output.txt",
    "upwords": "./texts/words/upp",
    "r-arpa": "./dictionary/alm.arpa",
    "r-abbr": "./dictionary/alm.abbr",
    "abbrs": "./texts/abbrs/abbrs.txt",
    "alters": "./texts/alters/yoficator.txt",
    "mix-restwords": "./similars/letters.txt",
    "goodwords": "./texts/whitelist/words.txt",
    "badwords": "./texts/blacklist/garbage.txt",
    "pilots": ["","","","","","","","","","","a","i","o","e","g"],
    "alphabet": "abcdefghijklmnopqrstuvwxyz",
    "embedding-size": 28,
    "embedding": {
        "": 0, "": 1, "": 2, "": 3, "": 4, "": 5,
        "": 5, "": 6, "": 7, "": 8, "": 8, "": 9,
        "": 10, "": 11, "": 12, "": 0, "": 13, "": 14,
        "": 15, "": 16, "": 17, "": 18, "": 19, "": 20,
        "": 21, "": 21, "": 21, "": 22, "": 23, "": 22,
        "": 5, "": 24, "": 25, "<": 26, ">": 26, "~": 26,
        "-": 26, "+": 26, "=": 26, "*": 26, "/": 26, ":": 26,
        "%": 26, "|": 26, "^": 26, "&": 26, "#": 26, "'": 26,
        "\\": 26, "0": 27, "1": 27, "2": 27, "3": 27, "4": 27,
        "5": 27, "6": 27, "7": 27, "8": 27, "9": 27, "a": 0,
        "b": 2, "c": 15, "d": 4, "e": 5, "f": 18, "g": 3,
        "h": 12, "i": 8, "j": 6, "k": 9, "l": 10, "m": 11,
        "n": 12, "o": 0, "p": 14, "q": 13, "r": 14, "s": 15,
        "t": 16, "u": 24, "v": 21, "w": 22, "x": 19, "y": 17, "z": 7
    }
}

$ ./asc -r-json ./spell.json

Python

import asc

#      
asc.setThreads(0)
#        
asc.setOption(asc.options_t.uppers)
#   
asc.setOption(asc.options_t.ascSplit)
#   
asc.setOption(asc.options_t.ascAlter)
#      
asc.setOption(asc.options_t.ascESplit)
#      
asc.setOption(asc.options_t.ascRSplit)
#     
asc.setOption(asc.options_t.ascUppers)
#     
asc.setOption(asc.options_t.ascHyphen)
#    
asc.setOption(asc.options_t.ascWordRep)
#         
asc.setOption(asc.options_t.mixDicts)
#     ARPA -  ,  
asc.setOption(asc.options_t.confidence)

#      (        )
asc.setAlphabet("abcdefghijklmnopqrstuvwxyz")
#     (     )
asc.setPilots(["","","","","","","","","","","a","i","o","e","g"])
#     
asc.setSubstitutes({'p':'','c':'','o':'','t':'','k':'','e':'','a':'','h':'','x':'','b':'','m':''})

#       
f = open('./texts/whitelist/words.txt')
for word in f.readlines():
    word = word.replace("\n", "")
    asc.addGoodword(word)
f.close()

#       
f = open('./texts/blacklist/garbage.txt')
for word in f.readlines():
    word = word.replace("\n", "")
    asc.addBadword(word)
f.close()

#     
f = open('./output/alm.abbr')
for word in f.readlines():
    word = word.replace("\n", "")
    asc.addSuffix(word)
f.close()

#    ,   (, ,  ...)
f = open('./texts/abbrs/abbrs.txt')
for abbr in f.readlines():
    abbr = abbr.replace("\n", "")
    asc.addAbbr(abbr)
f.close()

#     ,       (, , ...)
f = open('./texts/words/upp/words.txt')
for word in f.readlines():
    word = word.replace("\n", "")
    asc.addUWord(word)
f.close()

#   
asc.addAlt("", "")

#       ,     (        )
f = open('./texts/alters/yoficator.txt')
for words in f.readlines():
    words = words.replace("\n", "")
    words = words.split('\t')
    asc.addAlt(words[0], words[1])
f.close()

def statusArpa(status):
    print("Read arpa", status)

def statusIndex(status):
    print("Build index", status)

#        ARPA
asc.readArpa("./dictionary/alm.arpa", statusArpa)

#    (38120      13    )
asc.setAdCw(38120, 13)

#      ( ,     )
asc.setEmbedding({
    "": 0, "": 1, "": 2, "": 3, "": 4, "": 5,
    "": 5, "": 6, "": 7, "": 8, "": 8, "": 9,
    "": 10, "": 11, "": 12, "": 0, "": 13, "": 14,
    "": 15, "": 16, "": 17, "": 18, "": 19, "": 20,
    "": 21, "": 21, "": 21, "": 22, "": 23, "": 22,
    "": 5, "": 24, "": 25, "<": 26, ">": 26, "~": 26,
    "-": 26, "+": 26, "=": 26, "*": 26, "/": 26, ":": 26,
    "%": 26, "|": 26, "^": 26, "&": 26, "#": 26, "'": 26,
    "\\": 26, "0": 27, "1": 27, "2": 27, "3": 27, "4": 27,
    "5": 27, "6": 27, "7": 27, "8": 27, "9": 27, "a": 0,
    "b": 2, "c": 15, "d": 4, "e": 5, "f": 18, "g": 3,
    "h": 12, "i": 8, "j": 6, "k": 9, "l": 10, "m": 11,
    "n": 12, "o": 0, "p": 14, "q": 13, "r": 14, "s": 15,
    "t": 16, "u": 24, "v": 21, "w": 22, "x": 19, "y": 17, "z": 7
}, 28)

#     
asc.buildIndex(statusIndex)

f1 = open('./texts/test.txt')
f2 = open('./texts/output.txt', 'w')

for line in f1.readlines():
    res = asc.spell(line)
    f2.write("%s\n" % res[0])

f2.close()
f1.close()

PS Bagi yang tidak ingin mengumpulkan dan melatih sama sekali, saya bawa ASC versi web . Perlu juga diingat bahwa sistem untuk mengoreksi kesalahan ketik bukanlah sistem yang mahatahu dan tidak mungkin untuk memberi makan seluruh bahasa Rusia di sana. ASC tidak akan mengoreksi teks apa pun, perlu dilatih secara terpisah untuk setiap topik.

ANYKS Pemeriksa ejaan

Daftar fitur:

Sistem operasi yang didukung:

Kamus siap

Menguji

Bahan yang digunakan dalam pengujian

ASC vs JamSpell

hasil

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