code: package zemberek.ner;
  
  import java.io.IOException;
  import java.nio.file.Files;
  import java.nio.file.Path;
  import java.util.ArrayList;
  import java.util.HashMap;
  import java.util.List;
  import java.util.Map;
  import java.util.stream.Collectors;
  import zemberek.core.ScoredItem;
  import zemberek.core.data.CompressedWeights;
  import zemberek.core.data.WeightLookup;
  import zemberek.core.data.Weights;
  import zemberek.core.text.TextIO;
  import zemberek.core.text.TextUtil;
  import zemberek.morphology.TurkishMorphology;
  import zemberek.morphology.analysis.SingleAnalysis;
  import zemberek.morphology.analysis.WordAnalysis;
  import zemberek.morphology.analysis.WordAnalysisSurfaceFormatter;
  import zemberek.tokenization.TurkishTokenizer;
  
  /**
   * Multi-class averaged perceptron training.
   */
  public class PerceptronNer {
  
    private Map<String, ClassModel> model;
  
    private TurkishMorphology morphology;
  
    public PerceptronNer(Map<String, ClassModel> model, TurkishMorphology morphology) {
      this.model = model;
      this.morphology = morphology;
    }
  
    public void saveModelAsText(Path modelRoot) throws IOException {
      for (String key : model.keySet()) {
        model.get(key).saveText(modelRoot);
      }
      Files.write(
          modelRoot.resolve("types"),
          model.keySet().stream().sorted().collect(Collectors.toList()));
    }
  
    public void saveModelCompressed(Path modelRoot) throws IOException {
      for (String key : model.keySet()) {
        model.get(key).compressAndSave(modelRoot);
      }
      Files.write(
          modelRoot.resolve("types"),
          model.keySet().stream().sorted().collect(Collectors.toList()));
    }
  
    public static PerceptronNer loadModelFromResources(String name, TurkishMorphology morphology) {
      String resourceRoot = "/ner/model/" + name;
      try {
        List<String> types = TextIO.loadLinesFromResource(resourceRoot + "/types");
        Map<String, ClassModel> weightsMap = new HashMap<>();
        for (String type : types) {
          String resourcePath = resourceRoot + "/" + type + ".ner.model";
          ClassModel weights = ClassModel.loadFromResource(type, resourcePath);
          weightsMap.put(weights.id, weights);
        }
        return new PerceptronNer(weightsMap, morphology);
      } catch (IOException e) {
        throw new RuntimeException(e);
      }
    }
  
    public static PerceptronNer loadModel(Path modelRoot, TurkishMorphology morphology)
        throws IOException {
      Map<String, ClassModel> weightsMap = new HashMap<>();
      List<Path> files = Files.walk(modelRoot, 1)
          .filter(s -> s.toFile().getName().endsWith(".ner.model"))
          .collect(Collectors.toList());
      for (Path file : files) {
        ClassModel weights = ClassModel.load(file);
        weightsMap.put(weights.id, weights);
      }
      return new PerceptronNer(weightsMap, morphology);
    }
  
    public static ScoredItem<String> predictTypeAndPosition(
        Map<String, ClassModel> model,
        List<String> sparseKeys) {
  
      List<ScoredItem<String>> scores = new ArrayList<>();
  
      // find score for each class.
      for (String out : model.keySet()) {
        ClassModel o = model.get(out);
        float score = 0f;
        for (String s : sparseKeys) {
          score += o.sparseWeights.get(s);
        }
        scores.add(new ScoredItem<>(out, score));
      }
  
      // return max.
      return scores.stream().max((a, b) -> Float.compare(a.score, b.score)).get();
    }
  
    public NerDataSet evaluate(NerDataSet set) {
  
      List<NerSentence> resultSentences = new ArrayList<>();
  
      for (NerSentence sentence : set.sentences) {
        List<NerToken> predictedTokens = new ArrayList<>();
  
        for (int i = 0; i < sentence.tokens.size(); i++) {
  
          NerToken currentToken = sentence.tokens.get(i);
  
          FeatureData data = new FeatureData(morphology, sentence, i);
          List<String> sparseInputs = data.getTextualFeatures();
  
          if (i > 0) {
            sparseInputs.add("PreType=" + predictedTokens.get(i - 1).tokenId);
          }
          if (i > 1) {
            sparseInputs.add("2PreType=" + predictedTokens.get(i - 2).tokenId);
          }
          if (i > 2) {
            sparseInputs.add("3PreType=" + predictedTokens.get(i - 3).tokenId);
          }
  
          ScoredItem<String> predicted = PerceptronNer.predictTypeAndPosition(model, sparseInputs);
  
          NerToken predictedToken = NerToken.fromTypePositionString(
              currentToken.index, currentToken.word, currentToken.normalized, predicted.item);
          predictedTokens.add(predictedToken);
  
        }
        NerSentence predictedSentence = new NerSentence(sentence.content, predictedTokens);
        resultSentences.add(predictedSentence);
      }
      return new NerDataSet(resultSentences);
    }
  
    public NerSentence findNamedEntities(String sentence, List<String> words) {
      List<NerToken> tokens = new ArrayList<>();
      int index = 0;
      for (String word : words) {
        NerToken token = new NerToken(
            index,
            word,
            NerDataSet.normalizeForNer(word), NerDataSet.OUT_TOKEN_TYPE, NePosition.OUTSIDE);
        tokens.add(token);
        index++;
      }
  
      NerSentence nerSentence = new NerSentence(sentence, tokens);
  
      List<NerToken> predictedTokens = new ArrayList<>();
  
      for (int i = 0; i < nerSentence.tokens.size(); i++) {
  
        NerToken currentToken = nerSentence.tokens.get(i);
  
        FeatureData data = new FeatureData(morphology, nerSentence, i);
        List<String> sparseInputs = data.getTextualFeatures();
  
        if (i > 0) {
          sparseInputs.add("PreType=" + predictedTokens.get(i - 1).tokenId);
        }
        if (i > 1) {
          sparseInputs.add("2PreType=" + predictedTokens.get(i - 2).tokenId);
        }
        if (i > 2) {
          sparseInputs.add("3PreType=" + predictedTokens.get(i - 3).tokenId);
        }
  
        ScoredItem<String> predicted = predictTypeAndPosition(model, sparseInputs);
  
        NerToken predictedToken = NerToken.fromTypePositionString(
            currentToken.index, currentToken.word, currentToken.normalized, predicted.item);
        predictedTokens.add(predictedToken);
  
      }
      return new NerSentence(nerSentence.content, predictedTokens);
    }
  
    public NerSentence findNamedEntities(String sentence) {
      return findNamedEntities(sentence, TurkishTokenizer.DEFAULT.tokenizeToStrings(sentence));
    }
  
    public static class ClassModel {
  
      String id;
      WeightLookup sparseWeights = new Weights();
      List<DenseWeights> denseWeights = new ArrayList<>();
  
      public ClassModel(String id) {
        this.id = id;
      }
  
      public ClassModel(String id, WeightLookup sparseWeights) {
        this.id = id;
        this.sparseWeights = sparseWeights;
      }
  
      void updateSparse(List<String> inputs, float value) {
        if (sparseWeights instanceof CompressedWeights) {
          throw new IllegalStateException("Weights seems to be compressed. Cannot update weights.");
        }
        Weights w = (Weights) sparseWeights;
        for (String input : inputs) {
          w.increment(input, value);
        }
      }
  
      ClassModel copy() {
        if (sparseWeights instanceof CompressedWeights) {
          throw new IllegalStateException("Weights seems to be compressed. Cannot copy.");
        }
        Weights w = (Weights) sparseWeights;
        ClassModel model = new ClassModel(id);
        model.sparseWeights = w.copy();
        List<DenseWeights> copy = new ArrayList<>();
        for (DenseWeights denseWeight : denseWeights) {
          copy.add(new DenseWeights(denseWeight.id, denseWeight.weights.clone()));
        }
        model.denseWeights = copy;
        return model;
      }
  
      public void saveText(Path outRoot) throws IOException {
        if (sparseWeights instanceof CompressedWeights) {
          throw new IllegalStateException("Weights seems to be compressed. Cannot copy.");
        }
        Weights w = (Weights) sparseWeights;
        Path file = outRoot.resolve(id + ".ner.model");
        w.saveAsText(file);
      }
  
      public void compressAndSave(Path outRoot) throws IOException {
        if (sparseWeights instanceof CompressedWeights) {
          throw new IllegalStateException(
              "Weights seems to be compressed. Cannot compress it again.");
        }
        Weights w = (Weights) sparseWeights;
        CompressedWeights cw = w.compress();
        Path file = outRoot.resolve(id + ".ner.model");
        cw.serialize(file);
      }
  
      public static ClassModel load(Path modelFile) throws IOException {
        String id = modelFile.toFile().getName().replace(".ner.model", "");
        WeightLookup weightLookup;
        if (CompressedWeights.isCompressed(modelFile)) {
          weightLookup = CompressedWeights.deserialize(modelFile);
        } else {
          weightLookup = Weights.loadFromFile(modelFile);
        }
        return new ClassModel(id, weightLookup);
      }
  
      public static ClassModel loadFromResource(String id, String resourcePath) throws IOException {
  
        WeightLookup weightLookup;
        if (CompressedWeights.isCompressed(resourcePath)) {
          weightLookup = CompressedWeights.deserialize(resourcePath);
        } else {
          weightLookup = Weights.loadFromResource(resourcePath);
        }
        return new ClassModel(id, weightLookup);
      }
    }
  
    public static class DenseWeights {
  
      String id;
      float[] weights;
  
      public DenseWeights(String id, float[] weights) {
        this.id = id;
        this.weights = weights;
      }
    }
  
    static class FeatureData {
  
      static WordAnalysisSurfaceFormatter formatter = new WordAnalysisSurfaceFormatter();
      TurkishMorphology morphology;
      String currentWord;
      String currentWordOrig;
      String nextWord;
      String nextWordOrig;
      String nextWord2;
      String nextWord2Orig;
      String previousWord;
      String previousWordOrig;
      String previousWord2;
      String previousWord2Orig;
  
      FeatureData(
          TurkishMorphology morphology,
          NerSentence sentence,
          int index) {
        this.morphology = morphology;
        List<NerToken> tokens = sentence.tokens;
        this.currentWord = tokens.get(index).normalized;
        this.currentWordOrig = tokens.get(index).word;
        if (index == tokens.size() - 1) {
          //this.nextWord = "</s>";
          //this.nextWord2 = "</s>";
          //this.nextWordOrig = "</s>";
          //this.nextWord2Orig = "</s>";
        } else if (index == tokens.size() - 2) {
          this.nextWord = tokens.get(index + 1).normalized;
          //this.nextWord2 = "</s>";
          this.nextWordOrig = tokens.get(index + 1).word;
          this.nextWord2Orig = tokens.get(index + 1).word;
        } else {
          this.nextWord = tokens.get(index + 1).normalized;
          this.nextWord2 = tokens.get(index + 2).normalized;
          this.nextWordOrig = tokens.get(index + 1).word;
          this.nextWord2Orig = tokens.get(index + 1).word;
        }
        if (index == 0) {
          //this.previousWord = "<s>";
          //this.previousWord2 = "<s>";
          //this.previousWordOrig = "<s>";
          //this.previousWord2Orig = "<s>";
        } else if (index == 1) {
          this.previousWord = tokens.get(index - 1).normalized;
          //this.previousWord2 = "<s>";
          this.previousWordOrig = tokens.get(index - 1).word;
          this.previousWord2Orig = tokens.get(index - 1).word;
        } else {
          this.previousWord = tokens.get(index - 1).normalized;
          this.previousWord2 = tokens.get(index - 2).normalized;
          this.previousWordOrig = tokens.get(index - 1).word;
          this.previousWord2Orig = tokens.get(index - 1).word;
        }
      }
  
      void morphologicalFeatures(String word, String featurePrefix, List<String> features) {
        if (word == null) {
          return;
        }
        WordAnalysis analyses = morphology.analyze(word);
        SingleAnalysis longest =
            analyses.analysisCount() > 0 ?
                analyses.getAnalysisResults().get(analyses.analysisCount() - 1) :
                SingleAnalysis.unknown(word);
        for (SingleAnalysis analysis : analyses) {
          if (analysis.isUnknown()) {
            return;
          }
          if (analysis == longest) {
            continue;
          }
          List<String> cLemmas = analysis.getLemmas();
          List<String> lLemmas = longest.getLemmas();
  
          if (cLemmas.get(cLemmas.size() - 1).length() >
              lLemmas.get(lLemmas.size() - 1).length()) {
            longest = analysis;
          }
        }
        List<String> lemmas = longest.getLemmas();
        features.add(featurePrefix + "Stem:" + longest.getStem());
        String ending = longest.getEnding();
        if (ending.length() > 0) {
          features.add(featurePrefix + "Ending:" + ending);
        }
        features.add(featurePrefix + "LongLemma:" + lemmas.get(lemmas.size() - 1));
        features.add(featurePrefix + "POS:" + longest.getPos());
        features.add(featurePrefix + "LastIg:" + longest.getLastGroup().lexicalForm());
        //features.add(featurePrefix + "ContainsProper:" + containsProper);
      }
  
      List<String> getTextualFeatures() {
  
        List<String> features = new ArrayList<>();
        features.add("CW:" + currentWord);
        features.add("NW:" + nextWord);
        features.add("CW-NW:" + currentWord + nextWord);
        features.add("2NW:" + nextWord2);
        features.add("CW+NW+2NW:" + currentWord + nextWord + nextWord2);
  
        features.add("PW:" + previousWord);
        features.add("2PW:" + previousWord2);
        features.add("CW-PW:" + currentWord + previousWord);
        features.add("CW-PW-2PW:" + currentWord + previousWord + previousWord);
  
        features.add("PW-CW-NW:" + previousWord + currentWord + nextWord);
  
        wordFeatures(currentWordOrig, "CW", features);
        wordFeatures(previousWordOrig, "PW", features);
        wordFeatures(nextWordOrig, "NW", features);
  
        morphologicalFeatures(currentWordOrig, "CW", features);
        morphologicalFeatures(previousWordOrig, "PW", features);
        morphologicalFeatures(nextWordOrig, "NW", features);
  
        String cwLast2 =
            currentWord.length() > 2 ? currentWord.substring(currentWord.length() - 2) : "";
        if (cwLast2.length() > 0) {
          features.add("CWLast2:" + cwLast2);
        }
        String cwLast3 =
            currentWord.length() > 3 ? currentWord.substring(currentWord.length() - 3) : "";
        if (cwLast3.length() > 0) {
          features.add("CWLast3:" + cwLast3);
        }
  
        String cwFirst2 = currentWord.length() > 2 ? currentWord.substring(0, 2) : "";
        if (cwFirst2.length() > 0) {
          features.add("CWFirst2:" + cwFirst2);
        }
        String cwFirst3 = currentWord.length() > 3 ? currentWord.substring(0, 3) : "";
        if (cwFirst3.length() > 0) {
          features.add("CWFirst3:" + cwFirst3);
        }
  
        return features;
      }
  
      void wordFeatures(String word, String featurePrefix, List<String> features) {
        if (word == null) {
          return;
        }
        features.add(featurePrefix + "Upper:" + Character.isUpperCase(word.charAt(0)));
        features.add(featurePrefix + "Punct:" + (word.length() == 1));
        boolean allCap = true;
        for (char c : word.toCharArray()) {
          if (!Character.isUpperCase(c)) {
            allCap = false;
            break;
          }
        }
        features.add(featurePrefix + "AllCap:" + allCap);
        String s = TextUtil.normalizeApostrophes(word);
        int apostropheIndex = s.indexOf('\'');
        features.add(featurePrefix + "Apost:" + (apostropheIndex >= 0));
        if (apostropheIndex >= 0) {
          String stem = word.substring(0, apostropheIndex);
          String ending = word.substring(apostropheIndex + 1);
          features.add(featurePrefix + "Stem:" + stem);
          features.add(featurePrefix + "Ending:" + ending);
        }
      }
    }
  
  }
  