From bb167db72b98b027dbaa51b7c82e455d73f1876b Mon Sep 17 00:00:00 2001 From: ahmetaa Date: Wed, 7 Nov 2018 23:28:37 +0300 Subject: [PATCH] =?UTF-8?q?Biti=C5=9Fik=20kelimelerin=20ayr=C4=B1lmas?= =?UTF-8?q?=C4=B1=20durumunda=20birden=20fazla=20=C3=B6neri=20durumunda=20?= =?UTF-8?q?s=C4=B1ralama=20i=C3=A7in=20tek=20kelimelik=20dil=20modeli=20ol?= =?UTF-8?q?as=C4=B1l=C4=B1klar=C4=B1n=C4=B1=20kulland=C4=B1m.=20Ancak=20bu?= =?UTF-8?q?=20=C3=A7ok=20do=C4=9Fru=20=C3=A7al=C4=B1=C5=9Fm=C4=B1yor.=20No?= =?UTF-8?q?rmalde=202=20kelimelik=20dil=20modeli=20olas=C4=B1l=C4=B1klar?= =?UTF-8?q?=C4=B1=20kullan=C4=B1lmas=C4=B1=20gerekirdi.=20=C5=9Eu=20an=20i?= =?UTF-8?q?=C3=A7in=20bu=20m=C3=BCmk=C3=BCn=20olmad=C4=B1=C4=9F=C4=B1ndan?= =?UTF-8?q?=20bu=20=C5=9Fekil=20idare=20edebiliriz.?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../lib/runtime/zemberek-lo.jar | Bin 11778953 -> 11778975 bytes .../spellchecker/ZemberekSpellCheck.java | 67 +++++++++++++++--- 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java.util.ArrayList; +import java.util.Collections; import java.util.List; +import java.util.stream.Collectors; +import zemberek.core.ScoredItem; import zemberek.core.turkish.RootAttribute; +import zemberek.lm.LmVocabulary; +import zemberek.lm.NgramLanguageModel; import zemberek.morphology.TurkishMorphology; import zemberek.normalization.TurkishSpellChecker; @@ -12,6 +18,7 @@ public class ZemberekSpellCheck implements TurkishLinguist { private TurkishMorphology morphology; private TurkishSpellChecker spellChecker; + private NgramLanguageModel uniGramLanguageModel; private ZemberekSpellCheck() { this.morphology = TurkishMorphology.createWithDefaults(); @@ -21,6 +28,8 @@ private ZemberekSpellCheck() { // so that informal or out of official Turkish dictionary words are not allowed. this.spellChecker.setAnalysisPredicate( a -> !a.getDictionaryItem().hasAnyAttribute(RootAttribute.Ext, RootAttribute.Informal)); + this.uniGramLanguageModel = spellChecker.getUnigramLanguageModel(); + } catch (IOException e) { e.printStackTrace(); } @@ -31,33 +40,69 @@ public static ZemberekSpellCheck getInstance() { } public boolean isCorrect(String w) { - int indexOfDash = w.indexOf("-"); + String input = removePunctuation(w); + int indexOfDash = input.indexOf("-"); if (indexOfDash != -1) { - String w1 = w.substring(0, indexOfDash); - String w2 = w.substring(indexOfDash + 1); + String w1 = input.substring(0, indexOfDash); + String w2 = input.substring(indexOfDash + 1); if (spellChecker.check(w1) && spellChecker.check(w2)) { return true; } } - return spellChecker.check(removePunctuation(w)); + return spellChecker.check(input); } public List getSuggestions(String s) { + List suggestions = spellChecker.suggestForWord(removePunctuation(s)); - for (int i = 1; i <= s.length() - 2; i++) { - String s1 = s.substring(0, i); - String s2 = s.substring(i); - if (isCorrect(s1) && isCorrect(s2)) { - suggestions.add(0, s1 + " " + s2); - } - } if (suggestions.size() > 7) { return suggestions.subList(0, 7); } + suggestions.addAll(splitWordSuggestions(s)); return suggestions; } private String removePunctuation(String s) { return s.replaceAll("\\p{Punct}+$", ""); } + + private List splitWordSuggestions(String s) { + + // Prevent small or large inputs. + if (s.length() < 3 || s.length() > 25) { + return Collections.emptyList(); + } + + // Apply brute force splitting, and use uni-gram probabilities for ranking multiple scores. + // Normally using a higher order language model would be the correct approach + // for ranking but that is not available. + List> suggestions = new ArrayList<>(3); + LmVocabulary vocabulary = uniGramLanguageModel.getVocabulary(); + + for (int i = 1; i < s.length() - 1; i++) { + String s1 = s.substring(0, i); + String s2 = s.substring(i); + if (isCorrect(s1) && isCorrect(s2)) { + float p1 = uniGramLanguageModel.getProbability(vocabulary.indexOf(s1)); + float p2 = uniGramLanguageModel.getProbability(vocabulary.indexOf(s2)); + suggestions.add(new ScoredItem<>(s1 + " " + s2, p1 + p2)); + } + } + + if (suggestions.size() == 0) { + return Collections.emptyList(); + } + + // Sort with scores. Higher scored item comes first. + suggestions.sort((a, b) -> Float.compare(b.score, a.score)); + + // Only top 3 + if (suggestions.size() > 3) { + suggestions = suggestions.subList(0, 3); + } + + return suggestions.stream().map(a -> a.item).collect(Collectors.toList()); + } + + }