private static void normalize(
final Collection<UnresolvedDependency> unresolvedDependencies,
final IntIntHashMap substitutions,
final Set<UnresolvedDependency> newUnresolvedDependencies,
final Collection<UnlabelledDependency> newResolvedDependencies) {
for (final UnresolvedDependency dep : unresolvedDependencies) {
final int newID = substitutions.getOrDefault(dep.argumentID, Integer.MIN_VALUE);
if (newID != Integer.MIN_VALUE) {
if (newID == dep.argumentID) {
newUnresolvedDependencies.add(dep);
} else {
newUnresolvedDependencies.add(
new UnresolvedDependency(
dep.getHead(),
dep.getCategory(),
dep.getArgNumber(),
newID,
dep.getPreposition()));
}
} else {
// Dependencies that don't get any attachment (e.g. in arbitrary control).
newResolvedDependencies.add(
new UnlabelledDependency(
dep.getHead(),
dep.getCategory(),
dep.getArgNumber(),
Collections.singletonList(dep.getHead()),
dep.getPreposition()));
}
}
}
/**
* Builds a term-phrase matrix in the same space as the main term-document matrix. If the
* processing context contains no phrases, {@link VectorSpaceModelContext#termPhraseMatrix} will
* remain <code>null</code>.
*/
public void buildTermPhraseMatrix(VectorSpaceModelContext context) {
final PreprocessingContext preprocessingContext = context.preprocessingContext;
final IntIntHashMap stemToRowIndex = context.stemToRowIndex;
final int[] labelsFeatureIndex = preprocessingContext.allLabels.featureIndex;
final int firstPhraseIndex = preprocessingContext.allLabels.firstPhraseIndex;
if (firstPhraseIndex >= 0 && stemToRowIndex.size() > 0) {
// Build phrase matrix
int[] phraseFeatureIndices = new int[labelsFeatureIndex.length - firstPhraseIndex];
for (int featureIndex = 0; featureIndex < phraseFeatureIndices.length; featureIndex++) {
phraseFeatureIndices[featureIndex] = labelsFeatureIndex[featureIndex + firstPhraseIndex];
}
final DoubleMatrix2D phraseMatrix =
TermDocumentMatrixBuilder.buildAlignedMatrix(
context, phraseFeatureIndices, termWeighting);
MatrixUtils.normalizeColumnL2(phraseMatrix, null);
context.termPhraseMatrix = phraseMatrix.viewDice();
}
}
/**
* Builds a sparse term-document-like matrix for the provided matrixWordIndices in the same term
* space as the original term-document matrix.
*/
static DoubleMatrix2D buildAlignedMatrix(
VectorSpaceModelContext vsmContext, int[] featureIndex, ITermWeighting termWeighting) {
final IntIntHashMap stemToRowIndex = vsmContext.stemToRowIndex;
if (featureIndex.length == 0) {
return new DenseDoubleMatrix2D(stemToRowIndex.size(), 0);
}
final DoubleMatrix2D phraseMatrix =
new SparseDoubleMatrix2D(stemToRowIndex.size(), featureIndex.length);
final PreprocessingContext preprocessingContext = vsmContext.preprocessingContext;
final int[] wordsStemIndex = preprocessingContext.allWords.stemIndex;
final int[] stemsTf = preprocessingContext.allStems.tf;
final int[][] stemsTfByDocument = preprocessingContext.allStems.tfByDocument;
final int[][] phrasesWordIndices = preprocessingContext.allPhrases.wordIndices;
final int documentCount = preprocessingContext.documents.size();
final int wordCount = wordsStemIndex.length;
for (int i = 0; i < featureIndex.length; i++) {
final int feature = featureIndex[i];
final int[] wordIndices;
if (feature < wordCount) {
wordIndices = new int[] {feature};
} else {
wordIndices = phrasesWordIndices[feature - wordCount];
}
for (int wordIndex = 0; wordIndex < wordIndices.length; wordIndex++) {
final int stemIndex = wordsStemIndex[wordIndices[wordIndex]];
final int index = stemToRowIndex.indexOf(stemIndex);
if (stemToRowIndex.indexExists(index)) {
final int rowIndex = stemToRowIndex.indexGet(index);
double weight =
termWeighting.calculateTermWeight(
stemsTf[stemIndex], stemsTfByDocument[stemIndex].length / 2, documentCount);
phraseMatrix.setQuick(rowIndex, i, weight);
}
}
}
return phraseMatrix;
}
/**
* Builds a term document matrix from data provided in the <code>context</code>, stores the result
* in there.
*/
public void buildTermDocumentMatrix(VectorSpaceModelContext vsmContext) {
final PreprocessingContext preprocessingContext = vsmContext.preprocessingContext;
final int documentCount = preprocessingContext.documents.size();
final int[] stemsTf = preprocessingContext.allStems.tf;
final int[][] stemsTfByDocument = preprocessingContext.allStems.tfByDocument;
final byte[] stemsFieldIndices = preprocessingContext.allStems.fieldIndices;
if (documentCount == 0) {
vsmContext.termDocumentMatrix = new DenseDoubleMatrix2D(0, 0);
vsmContext.stemToRowIndex = new IntIntHashMap();
return;
}
// Determine the index of the title field
int titleFieldIndex = -1;
final String[] fieldsName = preprocessingContext.allFields.name;
for (int i = 0; i < fieldsName.length; i++) {
if (Document.TITLE.equals(fieldsName[i])) {
titleFieldIndex = i;
break;
}
}
// Determine the stems we, ideally, should include in the matrix
int[] stemsToInclude = computeRequiredStemIndices(preprocessingContext);
// Sort stems by weight, so that stems get included in the matrix in the order
// of frequency
final double[] stemsWeight = new double[stemsToInclude.length];
for (int i = 0; i < stemsToInclude.length; i++) {
final int stemIndex = stemsToInclude[i];
stemsWeight[i] =
termWeighting.calculateTermWeight(
stemsTf[stemIndex], stemsTfByDocument[stemIndex].length / 2, documentCount)
* getWeightBoost(titleFieldIndex, stemsFieldIndices[stemIndex]);
}
final int[] stemWeightOrder =
IndirectSort.mergesort(
0, stemsWeight.length, new IndirectComparator.DescendingDoubleComparator(stemsWeight));
// Calculate the number of terms we can include to fulfill the max matrix size
final int maxRows = maximumMatrixSize / documentCount;
final DoubleMatrix2D tdMatrix =
new DenseDoubleMatrix2D(Math.min(maxRows, stemsToInclude.length), documentCount);
for (int i = 0; i < stemWeightOrder.length && i < maxRows; i++) {
final int stemIndex = stemsToInclude[stemWeightOrder[i]];
final int[] tfByDocument = stemsTfByDocument[stemIndex];
final int df = tfByDocument.length / 2;
final byte fieldIndices = stemsFieldIndices[stemIndex];
for (int j = 0; j < df; j++) {
double weight =
termWeighting.calculateTermWeight(tfByDocument[j * 2 + 1], df, documentCount);
weight *= getWeightBoost(titleFieldIndex, fieldIndices);
tdMatrix.set(i, tfByDocument[j * 2], weight);
}
}
// Convert stemsToInclude into tdMatrixStemIndices
final IntIntHashMap stemToRowIndex = new IntIntHashMap();
for (int i = 0; i < stemWeightOrder.length && i < tdMatrix.rows(); i++) {
stemToRowIndex.put(stemsToInclude[stemWeightOrder[i]], i);
}
// Store the results
vsmContext.termDocumentMatrix = tdMatrix;
vsmContext.stemToRowIndex = stemToRowIndex;
}
