ANYKS المدقق الإملائي

صورة



مرحبًا ، هذه هي مقالتي الثالثة عن Habré ، لقد كتبت سابقًا مقالًا عن نموذج لغة ALM . الآن ، أود أن أقدم لكم نظام التصحيح المطبعي ASC (المطبق على أساس ALM ).



نعم ، هناك عدد هائل من الأنظمة لتصحيح الأخطاء المطبعية ، فجميعهم لديهم نقاط قوتهم وضعفهم ، من الأنظمة المفتوحة يمكنني تحديد أحد أكثر JamSpell الواعدة ، وسنقارن معه. هناك أيضًا نظام مشابه من DeepPavlov ، والذي قد يفكر فيه الكثيرون ، لكنني لم أقم بتكوين صداقات معه.



قائمة الميزات:



  1. تصحيح الأخطاء في الكلمات مع اختلاف يصل إلى 4 مسافات ليفنشتاين.
  2. تصحيح الأخطاء المطبعية في الكلمات (إدخال ، حذف ، استبدال ، إعادة ترتيب) الأحرف.
  3. fication بالنظر إلى السياق.
  4. وضع حالة الحرف الأول من الكلمة لـ (أسماء العلم والألقاب) مع مراعاة السياق.
  5. تقسيم الكلمات المجمعة إلى كلمات منفصلة مع مراعاة السياق.
  6. يقوم بتحليل النص دون تصحيح النص الأصلي.
  7. ابحث في النص عن التواجد (أخطاء ، أخطاء مطبعية ، سياق غير صحيح).




أنظمة التشغيل المدعومة:



  • نظام التشغيل MacOS X
  • فري بي إس دي
  • لينكس


النظام مكتوب بلغة C ++ 11 ، ويوجد منفذ لـ Python3



قواميس جاهزة



اسم الحجم (جيجابايت) ذاكرة الوصول العشوائي (جيجابايت) الحجم N- غرام لغة
Wittenbell-3-big.asc 1.97 15.6 3 RU
ويتنبل 3-middle.asc 1.24 9.7 3 RU
mkneserney-3-middle.asc 1.33 9.7 3 RU
ويتنبل 3-مفرد 0.772 5.14 3 RU
ويتنبل -5-سينجل 1.37 10.7 خمسة RU


اختبارات



تم استخدام البيانات من مسابقة 2016 Dialog21 "التصحيح المطبعي" لاختبار النظام  . تم استخدام قاموس ثنائي مدرب للاختبار:  wittenbell-3-middle.asc

تم إجراء الاختبار الاحكام اعد الاتصال قياس FM
وضع التصحيح المطبعي 76.97 62.71 69.11
وضع تصحيح الخطأ 73.72 60.53 66.48


أعتقد أنه ليس من الضروري إضافة بيانات أخرى ، إذا رغبت في ذلك ، يمكن للجميع إعادة الاختبار ، وأرفق جميع المواد المستخدمة في الاختبار أدناه.



المواد المستخدمة في الاختبار







الآن ، من المثير للاهتمام مقارنة تشغيل الأنظمة لتصحيح الأخطاء المطبعية نفسها في ظروف متساوية ، سنقوم بتدريب اثنين من الأخطاء المطبعية المختلفة على نفس البيانات النصية وإجراء اختبار.



للمقارنة ، لنأخذ نظام تصحيح الخطأ المطبعي الذي ذكرته أعلاه ، JamSpell .



ASC مقابل JamSpell



التركيب
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




تدريب
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




اختبارات
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




النتائج



الحصول على النتائج
$ python3 evaluate.py ./texts/test.txt ./texts/correct.txt ./texts/output.txt




ASC

الاحكام اعد الاتصال قياس FM
92.13 82.51 87.05


JamSpell

الاحكام اعد الاتصال قياس FM
77.87 63.36 69.87


من السمات الرئيسية لـ ASC التعلم من البيانات القذرة. من المستحيل عمليا العثور على نصوص جماعية بدون أخطاء وأخطاء مطبعية في الوصول المفتوح. لا يكفي إصلاح تيرابايت من البيانات يدويًا ، لكنك تحتاج إلى التعامل معها بطريقة ما.



مبدأ التدريس الذي أقدمه



  1. تجميع نموذج لغوي باستخدام بيانات قذرة
  2. نقوم بإزالة جميع الكلمات النادرة و N-grams في نموذج اللغة المُجمع
  3. نضيف كلمات مفردة للتشغيل الصحيح لنظام تصحيح الأخطاء المطبعية.
  4. تجميع قاموس ثنائي


هيا بنا نبدأ



لنفترض أن لدينا مجموعات متعددة من موضوعات مختلفة ، فمن المنطقي تدريبهم بشكل منفصل ثم دمجهم.



تجميع الهيكل باستخدام 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- —
  • smoothingwittenbell ( , - )
  • 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)


,



تقليم الهيكل المُجمَّع باستخدام 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 — ( )
  • smoothingwittenbell ( , - )
  • 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)




دمج البيانات المجمعة مع 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 — ( )
  • smoothingwittenbell ( , - )
  • 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)




تعلم نموذج اللغة مع 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 — ( )
  • smoothingwittenbell
  • 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)




تدريب ASC المدقق الإملائي
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 — ( )
  • smoothingwittenbell ( , - )
  • 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)




أفهم أنه لن يتمكن كل شخص من تدريب المفردات الثنائية الخاصة به ؛ وهذا يتطلب مجموعة نصية وموارد حاسوبية كبيرة. لذلك ، فإن ASC قادر على العمل مع ملف ARPA واحد فقط باعتباره القاموس الرئيسي.



مثال على العمل
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()




ملاحظة بالنسبة لأولئك الذين لا يرغبون في جمع أي شيء وتدريبه على الإطلاق ، طرحت نسخة الويب من ASC . يجب أن يؤخذ في الاعتبار أيضًا أن نظام تصحيح الأخطاء المطبعية ليس نظامًا كلي المعرفة ومن المستحيل إطعام اللغة الروسية بأكملها هناك. لن تقوم ASC بتصحيح أي نصوص ، فمن الضروري التدريب بشكل منفصل لكل موضوع.



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