code: package zemberek.ner;
  
  import com.google.common.collect.Sets;
  import java.io.IOException;
  import java.nio.file.Files;
  import java.nio.file.Path;
  import java.nio.file.Paths;
  import java.util.ArrayList;
  import java.util.LinkedHashSet;
  import java.util.List;
  import java.util.Set;
  import zemberek.core.logging.Log;
  import zemberek.core.text.BlockTextLoader;
  import zemberek.core.text.TextChunk;
  import zemberek.core.turkish.SecondaryPos;
  import zemberek.morphology.TurkishMorphology;
  import zemberek.morphology.analysis.SingleAnalysis;
  import zemberek.morphology.analysis.WordAnalysis;
  import zemberek.morphology.lexicon.DictionaryItem;
  import zemberek.morphology.lexicon.RootLexicon;
  import zemberek.ner.NerDataSet.AnnotationStyle;
  import zemberek.normalization.TextCleaner;
  
  public class CorpusNerCollector {
  
    public static void main(String[] args) throws IOException {
  
      Path corporaRoot = Paths.get("/media/ahmetaa/depo/corpora");
      Path corpusDirList = corporaRoot.resolve("ner-list");
      Path outRoot = Paths.get("/media/ahmetaa/depo/ner/out");
  
      Files.createDirectories(outRoot);
  
      BlockTextLoader corpusProvider = BlockTextLoader
          .fromDirectoryRoot(corporaRoot, corpusDirList, 10_000);
  
      // assumes you generated a model in my-model directory.
      Path modelRoot = Paths.get("my-model");
  
      TurkishMorphology morphology = TurkishMorphology
          .builder()
          .setLexicon(RootLexicon.getDefault())
          .disableUnidentifiedTokenAnalyzer()
          .build();
  
      PerceptronNer ner = PerceptronNer.loadModel(modelRoot, morphology);
  
      Set<String> illegal = Sets.newHashSet(".", ",", "!", "?", ":");
  
      List<String> lines = new ArrayList<>();
      int c = 0;
      int k = 0;
      for (TextChunk chunk : corpusProvider) {
        LinkedHashSet<String> sentences =
            new LinkedHashSet<>(TextCleaner.cleanAndExtractSentences(chunk.getData()));
        for (String sentence : sentences) {
          if (sentence.length() > 100) {
            continue;
          }
          NerSentence result = ner.findNamedEntities(sentence);
          int neCount = result.getNamedEntities().size();
          List<NamedEntity> nes = result.getNamedEntities();
          boolean badNamedEntity = false;
          for (NamedEntity ne : nes) {
            for(NerToken token : ne.tokens) {
              if(illegal.contains(token.word)) {
                badNamedEntity = true;
                break;
              }
              WordAnalysis a = morphology.analyze(token.word);
              for (SingleAnalysis analysis : a) {
                DictionaryItem item = analysis.getDictionaryItem();
                if(item.secondaryPos!= SecondaryPos.Abbreviation &&
                    item.secondaryPos!= SecondaryPos.ProperNoun
                ) {
                  badNamedEntity = true;
                  break;
                }
              }
            }
            if(badNamedEntity) {
              break;
            }
          }
          if (badNamedEntity) {
            continue;
          }
          if (neCount > 0 && neCount < 3) {
            lines.add(result.getAsTrainingSentence(AnnotationStyle.BRACKET));
            c++;
            if (c == 1000) {
              Path out = outRoot.resolve(chunk.id + "-" + k);
              Files.write(out, lines);
              Log.info("%s created. ", out);
              lines = new ArrayList<>();
              c = 0;
              k++;
              if(k>10) {
                System.exit(0);
              }
            }
          }
        }
      }
    }
  
  }
  