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Stanford CoreNLP provides a set of natural language analysis tools which can take raw English language text input and give the base forms of words, their parts of speech, whether they are names of companies, people, etc., normalize dates, times, and numeric quantities, mark up the structure of sentences in terms of phrases and word dependencies, and indicate which noun phrases refer to the same entities. It provides the foundational building blocks for higher level text understanding applications.

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package edu.stanford.nlp.ie.crf;

import edu.stanford.nlp.math.ArrayMath;
import edu.stanford.nlp.sequences.SeqClassifierFlags;

/**
 * @author Mengqiu Wang
 */
public class NonLinearSecondOrderCliquePotentialFunction implements CliquePotentialFunction {

  private final double[][] inputLayerWeights4Edge; // first index is number of hidden units in layer one, second index is the input feature indices
  private final double[][] outputLayerWeights4Edge; // first index is the output class, second index is the number of hidden units
  private final double[][] inputLayerWeights; // first index is number of hidden units in layer one, second index is the input feature indices
  private final double[][] outputLayerWeights; // first index is the output class, second index is the number of hidden units
  private double[] layerOneCache, hiddenLayerCache;
  private double[] layerOneCache4Edge, hiddenLayerCache4Edge;
  private final SeqClassifierFlags flags;

  public NonLinearSecondOrderCliquePotentialFunction(double[][] inputLayerWeights4Edge, double[][] outputLayerWeights4Edge, double[][] inputLayerWeights, double[][] outputLayerWeights, SeqClassifierFlags flags) {
    this.inputLayerWeights4Edge = inputLayerWeights4Edge;
    this.outputLayerWeights4Edge = outputLayerWeights4Edge;
    this.inputLayerWeights = inputLayerWeights;
    this.outputLayerWeights = outputLayerWeights;
    this.flags = flags;
  }

  public double[] hiddenLayerOutput(double[][] inputLayerWeights, int[] nodeCliqueFeatures, SeqClassifierFlags aFlag, double[] featureVal, int cliqueSize) {
    double[] layerCache = null;
    double[] hlCache = null;
    int layerOneSize = inputLayerWeights.length;
    if (cliqueSize > 1) {
      if (layerOneCache4Edge == null || layerOneSize != layerOneCache4Edge.length)
        layerOneCache4Edge = new double[layerOneSize];
      layerCache = layerOneCache4Edge;
    } else {
      if (layerOneCache == null || layerOneSize != layerOneCache.length)
        layerOneCache = new double[layerOneSize];
      layerCache = layerOneCache;
    }
    for (int i = 0; i < layerOneSize; i++) {
      double[] ws = inputLayerWeights[i];
      double lOneW = 0;
      double dotProd = 0;
      for (int m = 0; m < nodeCliqueFeatures.length; m++) {
        dotProd = ws[nodeCliqueFeatures[m]];
        if (featureVal != null)
          dotProd *= featureVal[m];
        lOneW += dotProd;
      }
      layerCache[i] = lOneW;
    }
    if (!aFlag.useHiddenLayer)
      return layerCache;

    // transform layer one through hidden
    if (cliqueSize > 1) {
      if (hiddenLayerCache4Edge == null || layerOneSize != hiddenLayerCache4Edge.length)
        hiddenLayerCache4Edge = new double[layerOneSize];
      hlCache = hiddenLayerCache4Edge;
    } else {
      if (hiddenLayerCache == null || layerOneSize != hiddenLayerCache.length)
        hiddenLayerCache = new double[layerOneSize];
      hlCache = hiddenLayerCache;
    }
    for (int i = 0; i < layerOneSize; i++) {
      if (aFlag.useSigmoid) {
        hlCache[i] = sigmoid(layerCache[i]);
      } else {
        hlCache[i] = Math.tanh(layerCache[i]);
      }
    }
    return hlCache;
  }
  private static double sigmoid(double x) {
    return 1 / (1 + Math.exp(-x));
  }

  @Override
  public double computeCliquePotential(int cliqueSize, int labelIndex,
      int[] cliqueFeatures, double[] featureVal, int posInSent) {
    double output = 0.0;
    double[][] inputWeights, outputWeights = null;
    if (cliqueSize > 1) {
      inputWeights = inputLayerWeights4Edge;
      outputWeights = outputLayerWeights4Edge;
    } else {
      inputWeights = inputLayerWeights;
      outputWeights = outputLayerWeights;
    }
    double[] hiddenLayer = hiddenLayerOutput(inputWeights, cliqueFeatures, flags, featureVal, cliqueSize);

    int outputLayerSize = inputWeights.length / outputWeights[0].length;

    // transform the hidden layer to output layer through linear transformation
    if (flags.useOutputLayer) {
      double[] outputWs = null;
      if (flags.tieOutputLayer) {
        outputWs = outputWeights[0];
      } else {
        outputWs = outputWeights[labelIndex];
      }
      if (flags.softmaxOutputLayer) {
        outputWs = ArrayMath.softmax(outputWs);
      }
      for (int i = 0; i < inputWeights.length; i++) {
        if (flags.sparseOutputLayer || flags.tieOutputLayer) {
          if (i % outputLayerSize == labelIndex) {
            output += outputWs[ i / outputLayerSize ] * hiddenLayer[i];
          }
        } else {
          output += outputWs[i] * hiddenLayer[i];
        }
      }
    } else {
      output = hiddenLayer[labelIndex];
    }

    return output;
  }

}