code: package zemberek.normalization;
  
  import com.google.common.base.Charsets;
  import com.google.common.base.Splitter;
  import com.google.common.collect.HashMultimap;
  import java.io.IOException;
  import java.io.PrintWriter;
  import java.nio.charset.Charset;
  import java.nio.charset.StandardCharsets;
  import java.nio.file.Files;
  import java.nio.file.Path;
  import java.nio.file.Paths;
  import java.util.ArrayList;
  import java.util.HashSet;
  import java.util.LinkedHashSet;
  import java.util.List;
  import java.util.Set;
  import java.util.concurrent.Callable;
  import java.util.concurrent.CompletionService;
  import java.util.concurrent.ExecutorCompletionService;
  import java.util.concurrent.ExecutorService;
  import java.util.concurrent.TimeUnit;
  import java.util.stream.Collectors;
  import zemberek.core.ScoredItem;
  import zemberek.core.collections.FixedBitVector;
  import zemberek.core.collections.Histogram;
  import zemberek.core.concurrency.BlockingExecutor;
  import zemberek.core.logging.Log;
  import zemberek.core.text.BlockTextLoader;
  import zemberek.core.text.TextChunk;
  import zemberek.core.text.TextIO;
  import zemberek.core.text.TextUtil;
  import zemberek.core.turkish.SecondaryPos;
  import zemberek.core.turkish.TurkishAlphabet;
  import zemberek.langid.LanguageIdentifier;
  import zemberek.lm.compression.SmoothLm;
  import zemberek.morphology.TurkishMorphology;
  import zemberek.morphology.analysis.AnalysisCache;
  import zemberek.morphology.analysis.SingleAnalysis;
  import zemberek.morphology.analysis.WordAnalysis;
  import zemberek.morphology.lexicon.RootLexicon;
  import zemberek.morphology.morphotactics.TurkishMorphotactics;
  import zemberek.normalization.deasciifier.Deasciifier;
  import zemberek.tokenization.TurkishTokenizer;
  import zemberek.tokenization.Token;
  
  public class NormalizationScripts {
  
    public static void main(String[] args) throws Exception {
  
      //Path root = Paths.get("/home/aaa/data/normalization");
      Path root = Paths.get("/home/aaa/data/normalization");
      Path testRoot = root.resolve("test-large");
  
      Path incorrect = testRoot.resolve("incorrect");
      Path correct = testRoot.resolve("correct");
  
      Path asciiMapPath = testRoot.resolve("ascii-map");
  
      findAsciiEquivalentFromNoisyAndClean(
          root.resolve("vocab-clean"),
          root.resolve("vocab-noisy"),
          asciiMapPath
      );
  
      Path s = testRoot.resolve("split");
      Path lm = root.resolve("lm.slm");
  
      splitWords(
          root.resolve("vocab-clean"),
          root.resolve("vocab-noisy"),
          s,
          lm,
          asciiMapPath,
          NormalizationVocabularyGenerator.getTurkishMorphology(true),
          2);
  
      Path quesOut = testRoot.resolve("question-suffix");
      //getQuestionSuffixes(s, quesOut);
      //convertTweetData();
  
  /*    Path repetitive = testRoot.resolve("repetitions.hist.txt");
      multipleLetterRepetitionWords(incorrect, repetitive);*/
  
      Path corporaRoot = Paths.get("/media/ahmetaa/depo/corpora");
      Path tweetRoot = Paths.get("/media/ahmetaa/depo/corpora");
  /*
      cleanTwitterData(
          tweetRoot.resolve("tweets-20m.txt"),
          tweetRoot.resolve("tweets-20m-clean")
      );
  */
  
  
  /*    splitSingleFileCorpus(
          corporaRoot.resolve("tweets-20m-clean.nodup"),
          corporaRoot.resolve("tweets-20m"));*/
  
  /*    generateNormalizationVocabularies(
          NormalizationVocabularyGenerator.getTurkishMorphology(),
          root.resolve("vocab-clean"),
          root.resolve("vocab-noisy"),
          testRoot
      );*/
    }
  
    static void findAsciiEquivalentFromNoisyAndClean(
        Path cleanRoot,
        Path noisyRoot,
        Path outfile
    ) throws IOException {
  
      Histogram<String> correctFromClean = Histogram
          .loadFromUtf8File(cleanRoot.resolve("correct"), ' ');
      Histogram<String> incorrectFromNoisy = Histogram
          .loadFromUtf8File(noisyRoot.resolve("incorrect"), ' ');
      incorrectFromNoisy.removeSmaller(2);
  
      HashMultimap<String, String> mmClean = HashMultimap.create();
  
      for (String s : correctFromClean) {
        String a = TurkishAlphabet.INSTANCE.toAscii(s);
        if (a.equals(s)) {
          continue;
        }
        if (correctFromClean.contains(a)) {
          mmClean.put(a, s);
        }
      }
  
      HashMultimap<String, String> mm = HashMultimap.create();
  
      for (String s : correctFromClean) {
        String a = TurkishAlphabet.INSTANCE.toAscii(s);
        if (a.equals(s)) {
          continue;
        }
        if (incorrectFromNoisy.contains(a)) {
          mm.put(a, s);
        }
      }
  
      for (String s : incorrectFromNoisy) {
        String a = TurkishAlphabet.INSTANCE.toAscii(s);
        if (a.equals(s)) {
          continue;
        }
        if (mmClean.containsKey(a)) {
          mm.putAll(a, mmClean.get(a));
        }
      }
  
      List<String> lines = new ArrayList<>();
      for (String k : mm.keySet()) {
        lines.add(k + "=" + String.join(",", mm.get(k)));
      }
  
      Files.write(outfile, lines, StandardCharsets.UTF_8);
  
    }
  
  
    static void splitWords(
        Path noisyVocab,
        Path cleanVocab,
        Path splitFile,
        Path lmPath,
        Path asciiMapPath,
        TurkishMorphology morphology,
        int minWordCount)
        throws IOException {
  
      Set<String> asciiMapKeys = Files.readAllLines(asciiMapPath)
          .stream().map(s -> s.substring(0, s.indexOf('='))).collect(Collectors.toSet());
  
      SmoothLm lm = SmoothLm.builder(lmPath).logBase(Math.E).build();
      Log.info("Language model = %s", lm.info());
  
      Histogram<String> wordFreq = Histogram.loadFromUtf8File(noisyVocab.resolve("incorrect"), ' ');
      wordFreq.add(Histogram.loadFromUtf8File(cleanVocab.resolve("incorrect"), ' '));
      Log.info("%d words loaded.", wordFreq.size());
  
      wordFreq.removeSmaller(minWordCount);
      if (minWordCount > 1) {
        Log.info("%d words left after removing counts less than %d.",
            wordFreq.size(),
            minWordCount
        );
      }
  
      int unkIndex = lm.getVocabulary().getUnknownWordIndex();
  
      try (PrintWriter pw = new PrintWriter(splitFile.toFile(), "utf-8");
          PrintWriter pwFreq =
              new PrintWriter(splitFile.toFile().getAbsolutePath() + "freq", "utf-8")) {
        for (String word : wordFreq.getSortedList()) {
  
          if (asciiMapKeys.contains(word)) {
            continue;
          }
  
          if (word.length() < 5 || word.contains("-")) {
            continue;
          }
  
          List<ScoredItem<String>> k = new ArrayList<>();
  
          for (int i = 1; i < word.length() - 1; i++) {
            String head = word.substring(0, i);
            String tail = word.substring(i);
  
            if (noSplitTails.contains(tail)) {
              continue;
            }
  
            int hi = lm.getVocabulary().indexOf(head);
            int ti = lm.getVocabulary().indexOf(tail);
  
            if (hi == unkIndex || ti == unkIndex) {
              continue;
            }
  
            if ((tail.equals("de") || tail.equals("da")) && morphology.analyze(head).isCorrect()) {
              continue;
            }
  
            if (lm.ngramExists(hi, ti)) {
              k.add(new ScoredItem<>(head + " " + tail, lm.getProbability(hi, ti)));
            }
          }
  
          if (k.size() > 1) {
            k.sort((a, b) -> Double.compare(b.score, a.score));
          }
  
          if (k.size() > 0) {
            ScoredItem<String> best = k.get(0);
            if (best.score > -7) {
              pw.println(word + " = " + best.item);
              pwFreq.println(word + " = " + best.item + " " + wordFreq.getCount(word));
            }
          }
        }
      }
    }
  
    static void getQuestionSuffixes(Path in, Path out) throws IOException {
      List<String> splitLines = Files.readAllLines(in, Charsets.UTF_8);
      Histogram<String> endings = new Histogram<>();
      for (String splitLine : splitLines) {
        String[] tokens = splitLine.split("=");
        String s = tokens[1].trim();
        String[] t2 = s.split("[ ]");
        if (t2.length != 2) {
          System.out.println("Problem in " + splitLine);
          continue;
        }
        String suf = t2[1];
        if (suf.startsWith("mi") ||
            suf.startsWith("mu") ||
            suf.startsWith("mı") ||
            suf.startsWith("mü")
        ) {
          endings.add(t2[1]);
        }
      }
      for (String ending : endings.getSortedList()) {
        System.out.println(ending + " " + endings.getCount(ending));
      }
      for (String ending : endings.getSortedList()) {
        System.out.println(ending);
      }
  
    }
  
    static void convertTweetData() throws IOException {
      Path in = Paths.get("/home/aaa/Downloads/20milyontweet/all_tweets.txt");
      Path out = Paths.get("/home/aaa/Downloads/20milyontweet/all_tweets-utf8");
      List<String> lines = Files.readAllLines(in, Charset.forName("iso-8859-9"));
      Log.info("Writing.");
      Files.write(out, lines, StandardCharsets.UTF_8);
    }
  
    static void multipleLetterRepetitionWords(Path in, Path out) throws IOException {
      Histogram<String> noisyWords = Histogram.loadFromUtf8File(in, ' ');
      Histogram<String> repetitionWords = new Histogram<>();
      for (String w : noisyWords) {
        if (w.length() == 1) {
          continue;
        }
        int maxRepetitionCount = 1;
        int repetitionCount = 1;
        char lastChar = w.charAt(0);
        for (int i = 1; i < w.length(); i++) {
          char c = w.charAt(i);
          if (c == lastChar) {
            repetitionCount++;
          } else {
            if (repetitionCount > maxRepetitionCount) {
              maxRepetitionCount = repetitionCount;
            }
            repetitionCount = 0;
          }
          lastChar = c;
        }
        if (maxRepetitionCount > 1) {
          repetitionWords.set(w, noisyWords.getCount(w));
        }
      }
      repetitionWords.saveSortedByCounts(out, " ");
    }
  
    static LanguageIdentifier lid;
  
    static {
      try {
        lid = LanguageIdentifier.fromInternalModels();
      } catch (IOException e) {
        e.printStackTrace();
      }
    }
  
  
    static void cleanTwitterData(Path in, Path out)
        throws Exception {
  
      AnalysisCache cache = AnalysisCache
          .builder()
          .dynamicCacheSize(300_000, 500_000).build();
  
      TurkishMorphology morphology = TurkishMorphology
          .builder()
          .setCache(cache)
          .setLexicon(RootLexicon.getDefault())
          .disableUnidentifiedTokenAnalyzer()
          .build();
  
      int threadCount = Runtime.getRuntime().availableProcessors() / 2;
      if (threadCount > 20) {
        threadCount = 20;
      }
      ExecutorService executorService =
          new BlockingExecutor(threadCount);
      CompletionService<TwitterSaver> service =
          new ExecutorCompletionService<>(executorService);
  
      int blockSize = 20_000;
      BlockTextLoader loader = BlockTextLoader.fromPath(in, blockSize);
  
      Path foreign = Paths.get(out.toString() + ".foreign");
      TwitterSaver saver = new TwitterSaver(out, foreign, blockSize);
  
      int bc = 0;
      for (TextChunk block : loader) {
        service.submit(new TwitterTask(morphology, saver, block, bc));
        bc++;
      }
      executorService.shutdown();
      executorService.awaitTermination(1, TimeUnit.DAYS);
    }
  
    static class TwitterSaver {
  
      PrintWriter pwValid;
      PrintWriter pwForeign;
      int blockSize;
      int v = 0;
      int f = 0;
      int total = 0;
  
      TwitterSaver(Path valid, Path foreign, int blockSize)
          throws IOException {
        pwValid = new PrintWriter(valid.toFile(), "utf-8");
        pwForeign = new PrintWriter(foreign.toFile(), "utf-8");
        this.blockSize = blockSize;
      }
  
      void save(List<String> clean, List<String> foreign) {
        clean.forEach(pwValid::println);
        foreign.forEach(pwForeign::println);
        f += foreign.size();
        v += clean.size();
        total += blockSize;
        Log.info("Lines processed = %d valid = %d foreign = %d", total, v, f);
      }
    }
  
    static class TwitterTask implements Callable<TwitterSaver> {
  
      TurkishMorphology morphology;
      TwitterSaver saver;
      TextChunk block;
      int blockIndex;
  
      public TwitterTask(TurkishMorphology morphology,
          TwitterSaver saver, TextChunk block, int blockIndex) {
        this.morphology = morphology;
        this.saver = saver;
        this.block = block;
        this.blockIndex = blockIndex;
      }
  
      String set = "!.-><?(*)'\"_";
      FixedBitVector lookup = TextUtil.generateBitLookup(set);
  
      String removeMultipleSymbols(String s) {
        if (s.length() < 2) {
          return s;
        }
        StringBuilder sb = new StringBuilder(s.length());
        char previous = s.charAt(0);
        sb.append(previous);
  
        for (int i = 1; i < s.length(); i++) {
          char c = s.charAt(i);
          if (c < lookup.length && lookup.get(c) && previous == c) {
            continue;
          }
          previous = c;
          sb.append(c);
        }
        return sb.toString();
  
      }
  
      @Override
      public TwitterSaver call() {
        Log.info("Processing block %d", blockIndex);
        List<String> clean = new ArrayList<>();
        List<String> foreign = new ArrayList<>();
        for (String s : block) {
          s = s.trim();
          if (s.contains("... http://")) {
            continue;
          }
          // remove time information.
          if (s.contains("EEST")) {
            s = s.replaceAll("^[^-]+-[^-]+-", "").trim(); // replaces until after second -
          }
          s = s.replaceAll("[\\u0095\\u0085]", " ");
          s = TextUtil.normalizeApostrophes(s);
          s = TextUtil.normalizeQuotesHyphens(s);
          s = TextUtil.normalizeSpacesAndSoftHyphens(s);
          s = removeMultipleSymbols(s);
  
          List<String> tokens = TurkishTokenizer.DEFAULT.tokenize(s)
              .stream().filter(
                  k -> k.getType() != Token.Type.HashTag &&
                      k.getType() != Token.Type.Mention &&
                      k.getType() != Token.Type.URL &&
                      k.getText().indexOf('_') < 0 &&
                      k.getText().indexOf('#') < 0 &&
                      k.getText().indexOf('@') < 0 &&
                      !k.getText().equals("RT"))
              .map(Token::getText)
              .collect(Collectors.toList());
          String join = String.join(" ", tokens);
          if (join.trim().length() == 0) {
            continue;
          }
  
          boolean turkish = false;
          String lang = lid.identify(join);
          //TODO: here is a hack for javanese-indonesian language confusion.
          // twitter data I use seems to have lots of data with those languages.
          // we try to deasciify those and sometimes erroneously identify them as turkish afterwards.
          // to prevent this, if language is identified as jv or id, we skip.
          // to remove this hack we need a better language identification model for Turkish
          // that is trained with also noisy text.
          if (!lang.equals("jv") && !lang.equals("id") &&
              TurkishSentenceNormalizer.probablyRequiresDeasciifier(join)) {
            String k = Deasciifier.deasciify(join);
            // identify and check morphology to be sure.
            String l = lid.identify(join);
            if (l.equals("tr")) {
              List<String> words = Splitter.on(' ').splitToList(k);
              int accepted = 0;
              for (String word : words) {
                WordAnalysis a = morphology.analyze(word);
                if (a.isCorrect()) {
                  accepted++;
                }
              }
              if (accepted * 1d / words.size() >= 0.3) {
                turkish = true;
              }
            }
          } else {
            turkish = lang.equals("tr");
          }
          if (turkish) {
            clean.add(join);
          } else {
            foreign.add(join);
          }
        }
        saver.save(clean, foreign);
        return saver;
      }
    }
  
    static void splitSingleFileCorpus(Path in, Path outRoot) throws IOException {
      int blockSize = 100_000;
      BlockTextLoader loader = BlockTextLoader.fromPath(in, blockSize);
      Files.createDirectories(outRoot);
  
      int bc = 0;
      for (TextChunk block : loader) {
        String name = in.toFile().getName();
        Path blockPath = outRoot.resolve(name + "." + bc);
        Files.write(blockPath, block, StandardCharsets.UTF_8);
        bc++;
      }
    }
  
    static void generateNormalizationVocabularies(
        TurkishMorphology morphology,
        Path cleanRoot,
        Path noisyRoot,
        Path outRoot) throws IOException {
      Files.createDirectories(outRoot);
      Histogram<String> correctFromNoisy =
          Histogram.loadFromUtf8File(noisyRoot.resolve("correct"), ' ');
      Log.info("Correct from noisy Loaded");
      Histogram<String> correctFromClean =
          Histogram.loadFromUtf8File(cleanRoot.resolve("correct"), ' ');
      Log.info("Correct from clean Loaded");
      correctFromClean.removeSmaller(2);
      correctFromNoisy.removeSmaller(2);
  
      Histogram<String> zero = new Histogram<>();
      Histogram<String> zeroWordZeroLemma = new Histogram<>();
      Histogram<String> zeroWordLowLemma = new Histogram<>();
      Histogram<String> lowFreq = new Histogram<>();
      Histogram<String> lowFreqLowLemmaFreq = new Histogram<>();
      Histogram<String> unusualProper = new Histogram<>();
      Histogram<String> unusualRoots = new Histogram<>();
      Histogram<String> ignore = new Histogram<>();
  
      double nTotal = correctFromNoisy.totalCount();
      double cTotal = correctFromClean.totalCount();
  
      for (String s : correctFromNoisy) {
  
        if (s.contains(".")) {
          ignore.add(s);
          continue;
        }
  
        int nCount = correctFromNoisy.getCount(s);
        double nFreq = nCount / nTotal;
  
        WordAnalysis an = morphology.analyze(s);
        if (unusualProper(an)) {
          unusualProper.add(s, correctFromNoisy.getCount(s));
          continue;
        }
        if (unusualRoot(an)) {
          unusualRoots.add(s, correctFromNoisy.getCount(s));
          continue;
        }
  
        if (!correctFromClean.contains(s)) {
          zero.add(s, nCount);
          if (an.analysisCount() > 0) {
            Set<String> allLemmas = new HashSet<>();
            for (SingleAnalysis analysis : an) {
              allLemmas.addAll(analysis.getLemmas());
            }
  
            boolean none = true;
            boolean lowLemmaRatio = true;
            // TODO: this is not the best way. try extracting lemma frequencies from correct from clean
            for (String l : allLemmas) {
              if (correctFromClean.contains(l)) {
                none = false;
                double lnf = correctFromNoisy.getCount(l) / nTotal;
                double lcf = correctFromClean.getCount(l) / nTotal;
                if (lnf / lcf > 10) {
                  lowLemmaRatio = false;
                  break;
                }
              }
            }
  
            if (none) {
              zeroWordZeroLemma.add(s, nCount);
            }
            if (lowLemmaRatio) {
              zeroWordLowLemma.add(s, nCount);
            }
          }
          continue;
        }
  
        double cFreq = correctFromClean.getCount(s) / cTotal;
        if (nFreq / cFreq > 30) {
          lowFreq.add(s, nCount);
        }
  
      }
      Log.info("Saving Possibly incorrect words.");
      zero.saveSortedByCounts(noisyRoot.resolve("possibly-incorrect-zero"), " ");
      zeroWordZeroLemma
          .saveSortedByCounts(noisyRoot.resolve("possibly-incorrect-zero-no-lemma"), " ");
      zeroWordLowLemma
          .saveSortedByCounts(noisyRoot.resolve("possibly-incorrect-zero-low-lemma"), " ");
      lowFreq.saveSortedByCounts(noisyRoot.resolve("possibly-incorrect-lowfreq"), " ");
  
      Log.info("Creating vocabularies");
  
      // ----------- noisy ------------
      Histogram<String> noisy = new Histogram<>(1_000_000);
  
      Histogram<String> noisyFromCleanCorpora =
          Histogram.loadFromUtf8File(cleanRoot.resolve("incorrect"), ' ');
      Histogram<String> noisyFromNoisyCorpora =
          Histogram.loadFromUtf8File(noisyRoot.resolve("incorrect"), ' ');
      Log.info("Incorrect words loaded.");
      noisyFromCleanCorpora.removeSmaller(2);
      noisyFromNoisyCorpora.removeSmaller(2);
  
      noisy.add(noisyFromCleanCorpora);
      noisy.add(noisyFromNoisyCorpora);
  
      Histogram<String> possiblyIncorrect = new Histogram<>(1000_000);
      possiblyIncorrect.add(zeroWordZeroLemma);
      for (String lf : lowFreq) {
        if (!possiblyIncorrect.contains(lf)) {
          possiblyIncorrect.add(lf, zeroWordZeroLemma.getCount(lf));
        }
      }
  
      int threshold = 2;
  
      for (String z : zero) {
        int c = zero.getCount(z);
        if (!possiblyIncorrect.contains(z) && c > threshold) {
          possiblyIncorrect.add(z, c);
        }
      }
  
      Histogram<String> clean = new Histogram<>(1000_000);
      clean.add(correctFromClean);
      clean.add(correctFromNoisy);
  
      for (String s : clean) {
        if (s.contains(".")) {
          ignore.add(s);
        }
      }
      clean.removeAll(ignore);
  
      Histogram<String> asciiDuplicates = getAsciiDuplicates(clean);
      asciiDuplicates.saveSortedByCounts(outRoot.resolve("ascii-dups"), " ");
      possiblyIncorrect.add(asciiDuplicates);
  
      unusualProper.saveSortedByCounts(outRoot.resolve("unusual-proper"), " ");
      for (String s : unusualProper) {
        if (!possiblyIncorrect.contains(s)) {
          possiblyIncorrect.add(s, unusualProper.getCount(s));
        }
      }
      unusualRoots.saveSortedByCounts(outRoot.resolve("unusual-root"), " ");
      for (String s : unusualRoots) {
        if (!possiblyIncorrect.contains(s)) {
          possiblyIncorrect.add(s, unusualRoots.getCount(s));
        }
      }
  
      possiblyIncorrect.removeAll(ignore);
      clean.removeAll(asciiDuplicates);
      clean.removeAll(unusualProper);
      clean.removeAll(unusualRoots);
      clean.removeAll(possiblyIncorrect);
  
      Set<String> intersectionOfKeys = noisy.getIntersectionOfKeys(clean);
      int sharedKeyCount = intersectionOfKeys.size();
      if (sharedKeyCount > 0) {
        Log.warn("Incorrect and correct sets share %d keys", sharedKeyCount);
      }
      sharedKeyCount = noisy.getIntersectionOfKeys(possiblyIncorrect).size();
      if (sharedKeyCount > 0) {
        Log.warn("Incorrect and possibly incorrect sets share %d keys", sharedKeyCount);
      }
      sharedKeyCount = clean.getIntersectionOfKeys(possiblyIncorrect).size();
      if (sharedKeyCount > 0) {
        Log.warn("Correct and possibly incorrect sets share %d keys", sharedKeyCount);
      }
  
      Log.info("Saving sets.");
  
      clean.saveSortedByCounts(outRoot.resolve("correct"), " ");
      Log.info("Correct words saved.");
  
      noisy.saveSortedByCounts(outRoot.resolve("incorrect"), " ");
      Log.info("Incorrect words saved.");
  
      possiblyIncorrect.saveSortedByCounts(outRoot.resolve("possibly-incorrect"), " ");
      Log.info("Possibly Incorrect words saved.");
    }
  
    static Histogram<String> getAsciiDuplicates(Histogram<String> list) {
      Histogram<String> result = new Histogram<>(10_000);
      for (String s : list) {
        s = TurkishAlphabet.INSTANCE.normalizeCircumflex(s);
        String ascii = TurkishAlphabet.INSTANCE.toAscii(s);
        if (ascii.equals(s)) {
          continue;
        }
        if (list.contains(ascii)) {
          result.add(ascii, list.getCount(ascii));
        }
      }
      return result;
    }
  
    // TODO: all short proper nouns with a suffix are also candidates.
    static boolean unusualProper(WordAnalysis wa) {
      for (SingleAnalysis s : wa) {
        SecondaryPos spos = s.getDictionaryItem().secondaryPos;
        if (spos == SecondaryPos.ProperNoun || spos == SecondaryPos.Abbreviation) {
          if (!s.containsAnyMorpheme(
              TurkishMorphotactics.verb,
              TurkishMorphotactics.a3pl,
              TurkishMorphotactics.p1sg,
              TurkishMorphotactics.p2sg,
              TurkishMorphotactics.p1pl,
              TurkishMorphotactics.p2pl,
              TurkishMorphotactics.agt,
              TurkishMorphotactics.justLike,
              TurkishMorphotactics.dim,
              TurkishMorphotactics.p3pl)) {
            return false;
          }
        } else {
          return false;
        }
      }
      return true;
    }
  
    static boolean unusualRoot(WordAnalysis wa) {
      for (SingleAnalysis s : wa) {
        if (!unusualRoots.contains(s.getDictionaryItem().root)) {
          return false;
        }
      }
      return true;
    }
  
    static LinkedHashSet<String> unusualRoots = new LinkedHashSet<>();
    static LinkedHashSet<String> noSplitTails = new LinkedHashSet<>();
  
    static {
      try {
        unusualRoots = new LinkedHashSet<>(
            TextIO.loadLinesFromResource("/normalization/possible-noisy-roots"));
        noSplitTails = new LinkedHashSet<>(
            TextIO.loadLinesFromResource("/normalization/no-split-tails"));
      } catch (IOException e) {
        e.printStackTrace();
      }
    }
  
  
  }
  