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Carrot2 search results clustering framework. Minimal functional subset
(core algorithms and infrastructure, no document sources).
/*
* Carrot2 project.
*
* Copyright (C) 2002-2019, Dawid Weiss, Stanisław Osiński.
* All rights reserved.
*
* Refer to the full license file "carrot2.LICENSE"
* in the root folder of the repository checkout or at:
* http://www.carrot2.org/carrot2.LICENSE
*/
package org.carrot2.clustering.stc;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.Collections;
import java.util.Comparator;
import java.util.List;
import java.util.Locale;
import java.util.Map;
import org.carrot2.clustering.stc.GeneralizedSuffixTree.SequenceBuilder;
import org.carrot2.core.Cluster;
import org.carrot2.core.Document;
import org.carrot2.core.IClusteringAlgorithm;
import org.carrot2.core.LanguageCode;
import org.carrot2.core.ProcessingComponentBase;
import org.carrot2.core.ProcessingException;
import org.carrot2.core.attribute.AttributeNames;
import org.carrot2.core.attribute.CommonAttributes;
import org.carrot2.core.attribute.Init;
import org.carrot2.core.attribute.Internal;
import org.carrot2.core.attribute.Processing;
import org.carrot2.text.analysis.ITokenizer;
import org.carrot2.text.analysis.TokenTypeUtils;
import org.carrot2.text.clustering.IMonolingualClusteringAlgorithm;
import org.carrot2.text.clustering.MultilingualClustering;
import org.carrot2.text.linguistic.ILexicalData;
import org.carrot2.text.preprocessing.LabelFormatter;
import org.carrot2.text.preprocessing.PreprocessingContext;
import org.carrot2.text.preprocessing.pipeline.BasicPreprocessingPipeline;
import org.carrot2.text.preprocessing.pipeline.IPreprocessingPipeline;
import org.carrot2.util.attribute.Attribute;
import org.carrot2.util.attribute.AttributeLevel;
import org.carrot2.util.attribute.Bindable;
import org.carrot2.util.attribute.DefaultGroups;
import org.carrot2.util.attribute.Group;
import org.carrot2.util.attribute.Input;
import org.carrot2.util.attribute.Label;
import org.carrot2.util.attribute.Level;
import org.carrot2.util.attribute.Output;
import org.carrot2.util.attribute.Required;
import org.carrot2.util.attribute.constraint.DoubleRange;
import org.carrot2.util.attribute.constraint.ImplementingClasses;
import org.carrot2.util.attribute.constraint.IntRange;
import com.carrotsearch.hppc.BitSet;
import com.carrotsearch.hppc.BitSetIterator;
import com.carrotsearch.hppc.IntArrayList;
import com.carrotsearch.hppc.IntStack;
import org.carrot2.shaded.guava.common.base.Predicate;
import org.carrot2.shaded.guava.common.collect.Collections2;
import org.carrot2.shaded.guava.common.collect.Lists;
import org.carrot2.shaded.guava.common.collect.Maps;
/**
* Suffix Tree Clustering (STC) algorithm. Pretty much as described in: Oren Zamir,
* Oren Etzioni, Grouper: A Dynamic Clustering Interface to Web Search Results, 1999.
* Some liberties were taken wherever STC's description was not clear enough or where we
* thought some improvements could be made.
*/
@Bindable(prefix = "STCClusteringAlgorithm", inherit = CommonAttributes.class)
@Label("STC Clustering")
public final class STCClusteringAlgorithm extends ProcessingComponentBase implements
IClusteringAlgorithm
{
/** {@link Group} name. */
private final static String BASE_CLUSTERS = "Base clusters";
/** {@link Group} name. */
private final static String MERGING_AND_OUTPUT = "Merging and output";
/**
* Query that produced the documents. The query will help the algorithm to create
* better clusters. Therefore, providing the query is optional but desirable.
*/
@Processing
@Input
@Internal
@Attribute(key = AttributeNames.QUERY, inherit = true)
public String query = null;
/**
* Documents to cluster.
*/
@Processing
@Input
@Required
@Internal
@Attribute(key = AttributeNames.DOCUMENTS, inherit = true)
public List documents;
/**
* Clusters created by the algorithm.
*/
@Processing
@Output
@Internal
@Attribute(key = AttributeNames.CLUSTERS, inherit = true)
public List clusters = null;
/**
* Minimum word-document recurrences.
*/
@Processing
@Input
@Attribute
@IntRange(min = 2)
@Level(AttributeLevel.MEDIUM)
@Group(DefaultGroups.WORD_FILTERING)
public int ignoreWordIfInFewerDocs = 2;
/**
* Maximum word-document ratio. A number between 0 and 1, if a word exists in more
* snippets than this ratio, it is ignored.
*/
@Processing
@Input
@Attribute
@DoubleRange(min = 0, max = 1)
@Level(AttributeLevel.MEDIUM)
@Group(DefaultGroups.WORD_FILTERING)
public double ignoreWordIfInHigherDocsPercent = 0.9d;
/**
* Minimum base cluster score.
*/
@Processing
@Input
@Attribute
@DoubleRange(min = 0, max = 10)
@Level(AttributeLevel.ADVANCED)
@Group(BASE_CLUSTERS)
public double minBaseClusterScore = 2.0d;
/**
* Maximum base clusters count. Trims the base cluster array after N-th position for
* the merging phase.
*/
@Processing
@Input
@Attribute
@IntRange(min = 2)
@Level(AttributeLevel.ADVANCED)
@Group(BASE_CLUSTERS)
public int maxBaseClusters = 300;
/**
* Minimum documents per base cluster.
*/
@Processing
@Input
@Attribute
@IntRange(min = 2, max = 20)
@Level(AttributeLevel.ADVANCED)
@Group(BASE_CLUSTERS)
public int minBaseClusterSize = 2;
/**
* Maximum final clusters.
*/
@Processing
@Input
@Attribute
@IntRange(min = 1)
@Level(AttributeLevel.BASIC)
@Group(MERGING_AND_OUTPUT)
public int maxClusters = 15;
/**
* Base cluster merge threshold.
*/
@Processing
@Input
@Attribute
@DoubleRange(min = 0, max = 1)
@Level(AttributeLevel.ADVANCED)
@Group(MERGING_AND_OUTPUT)
public double mergeThreshold = 0.6d;
/**
* Maximum cluster phrase overlap.
*/
@Processing
@Input
@Attribute
@DoubleRange(min = 0, max = 1)
@Level(AttributeLevel.ADVANCED)
@Group(DefaultGroups.LABELS)
public double maxPhraseOverlap = 0.6d;
/**
* Minimum general phrase coverage. Minimum phrase coverage to appear in cluster
* description.
*/
@Processing
@Input
@Attribute
@DoubleRange(min = 0, max = 1)
@Level(AttributeLevel.ADVANCED)
@Group(DefaultGroups.LABELS)
public double mostGeneralPhraseCoverage = 0.5d;
/**
* Maximum words per label. Base clusters formed by phrases with more words than this
* ratio are trimmed.
*/
@Processing
@Input
@Attribute
@IntRange(min = 1)
@Level(AttributeLevel.BASIC)
@Group(DefaultGroups.LABELS)
public int maxDescPhraseLength = 4;
/**
* Maximum phrases per label. Maximum number of phrases from base clusters promoted
* to the cluster's label.
*/
@Processing
@Input
@Attribute
@IntRange(min = 1)
@Level(AttributeLevel.BASIC)
@Group(DefaultGroups.LABELS)
public int maxPhrases = 3;
/**
* Single term boost. A factor in calculation of the base cluster score. If greater
* then zero, single-term base clusters are assigned this value regardless of the
* penalty function.
*/
@Processing
@Input
@Attribute
@DoubleRange(min = 0)
@Level(AttributeLevel.MEDIUM)
@Group(BASE_CLUSTERS)
public double singleTermBoost = 0.5d;
/**
* Optimal label length. A factor in calculation of the base cluster score.
*/
@Processing
@Input
@Attribute
@IntRange(min = 1)
@Level(AttributeLevel.BASIC)
@Group(BASE_CLUSTERS)
public int optimalPhraseLength = 3;
/**
* Phrase length tolerance. A factor in calculation of the base cluster score.
*/
@Processing
@Input
@Attribute
@DoubleRange(min = 0.5)
@Level(AttributeLevel.MEDIUM)
@Group(BASE_CLUSTERS)
public double optimalPhraseLengthDev = 2.0d;
/**
* Document count boost. A factor in calculation of the base cluster score, boosting
* the score depending on the number of documents found in the base cluster.
*/
@Processing
@Input
@Attribute
@DoubleRange(min = 0)
@Level(AttributeLevel.MEDIUM)
@Group(BASE_CLUSTERS)
public double documentCountBoost = 1.0d;
/**
* Common preprocessing tasks handler.
*/
@Init
@Input
@Attribute
@Internal
@ImplementingClasses(classes = {
BasicPreprocessingPipeline.class
}, strict = false)
@Level(AttributeLevel.ADVANCED)
public IPreprocessingPipeline preprocessingPipeline = new BasicPreprocessingPipeline();
/**
* Balance between cluster score and size during cluster sorting. Value equal to 0.0
* will sort clusters based only on cluster size. Value equal to 1.0
* will sort clusters based only on cluster score.
*/
@Input
@Processing
@Attribute
@DoubleRange(min = 0.0, max = 1.0)
@Label("Size-Score sorting ratio")
@Level(AttributeLevel.MEDIUM)
@Group(DefaultGroups.CLUSTERS)
public double scoreWeight = 1.0;
/**
* Merge all stem-equivalent base clusters before running the merge phase.
*
* @see "http://issues.carrot2.org/browse/CARROT-1008"
*/
@Input
@Processing
@Attribute
@Label("Merge all stem-equivalent phrases when discovering base clusters")
@Level(AttributeLevel.MEDIUM)
@Group(DefaultGroups.CLUSTERS)
public boolean mergeStemEquivalentBaseClusters = true;
/**
* A helper for performing multilingual clustering.
*/
public final MultilingualClustering multilingualClustering = new MultilingualClustering();
/**
* Stores the preprocessing context during {@link #process()}.
*/
PreprocessingContext context;
/**
* Suffix tree and suffix tree input during {@link #process()}.
*/
GeneralizedSuffixTree.SequenceBuilder sb;
/**
* Helper class for computing merged cluster labels.
*
* @see STCClusteringAlgorithm#merge
*/
private static final class PhraseCandidate
{
final ClusterCandidate cluster;
final float coverage;
/** If false the phrase should not be selected (various criteria). */
boolean selected = true;
/** @see STCClusteringAlgorithm#markSubSuperPhrases(ArrayList) */
boolean mostGeneral = true;
/** @see STCClusteringAlgorithm#markSubSuperPhrases(ArrayList) */
boolean mostSpecific = true;
PhraseCandidate(ClusterCandidate c, float coverage)
{
this.cluster = c;
this.coverage = coverage;
}
}
/**
* Returns a collection of {@link PhraseCandidate}s that have
* {@link PhraseCandidate#selected} set to false.
*/
private final static Predicate notSelected = new Predicate()
{
public boolean apply(PhraseCandidate p)
{
return !p.selected;
}
};
/**
* Performs STC clustering of {@link #documents}.
*/
@Override
public void process() throws ProcessingException
{
// There is a tiny trick here to support multilingual clustering without
// refactoring the whole component: we remember the original list of documents
// and invoke clustering for each language separately within the
// IMonolingualClusteringAlgorithm implementation below. This is safe because
// processing components are not thread-safe by definition and
// IMonolingualClusteringAlgorithm forbids concurrent execution by contract.
final List originalDocuments = documents;
clusters = multilingualClustering.process(documents,
new IMonolingualClusteringAlgorithm()
{
public List process(List documents,
LanguageCode language)
{
STCClusteringAlgorithm.this.documents = documents;
STCClusteringAlgorithm.this.cluster(language);
return STCClusteringAlgorithm.this.clusters;
}
});
documents = originalDocuments;
}
/**
* Performs the actual clustering with an assumption that all documents are written in
* one language.
*/
private void cluster(LanguageCode language)
{
clusters = new ArrayList();
/*
* Step 1. Preprocessing: tokenization, stop word marking and stemming (if available).
*/
context = preprocessingPipeline.preprocess(documents, query, language);
/*
* Step 2: Create a generalized suffix tree from phrases in the input.
*/
sb = new GeneralizedSuffixTree.SequenceBuilder();
final int [] tokenIndex = context.allTokens.wordIndex;
final short [] tokenType = context.allTokens.type;
for (int i = 0; i < tokenIndex.length; i++)
{
/* Advance until the first real token. */
if (tokenIndex[i] == -1)
{
if ((tokenType[i] & (ITokenizer.TF_SEPARATOR_DOCUMENT | ITokenizer.TF_TERMINATOR)) != 0)
{
sb.endDocument();
}
continue;
}
/* We have the first token. Advance until non-token. */
final int s = i;
while (tokenIndex[i + 1] != -1) i++;
final int phraseLength = 1 + i - s;
if (phraseLength >= 1)
{
/* We have a phrase. */
sb.addPhrase(tokenIndex, s, phraseLength);
}
}
sb.buildSuffixTree();
/*
* Step 3: Find "base" clusters by looking up frequently recurring phrases in the
* generalized suffix tree.
*/
List baseClusters = createBaseClusters(sb);
/*
* Step 4: Merge base clusters that overlap too much to form final clusters.
*/
List mergedClusters = createMergedClusters(baseClusters);
/*
* Step 5: Create the junk (unassigned documents) cluster and create the final
* set of clusters in Carrot2 format.
*/
postProcessing(mergedClusters);
}
/**
* Memory cleanups.
*/
@Override
public void afterProcessing()
{
super.afterProcessing();
this.context = null;
this.sb = null;
}
/**
* Create base clusters. Base clusters are frequently occurring words and
* phrases. We extract them by walking the generalized suffix tree constructed for
* each phrase, and extracting paths from those internal tree states, that occurred in
* more than one document.
*/
private List createBaseClusters(SequenceBuilder sb)
{
/*
* Collect all phrases that will form base clusters,
* initially filtered to fulfill the minimum acceptance criteria.
*/
final List candidates = Lists.newArrayList();
// Walk the internal nodes of the suffix tree.
new GeneralizedSuffixTree.Visitor(sb, minBaseClusterSize) {
protected void visit(int state, int cardinality,
BitSet documents, IntStack path)
{
// Check minimum base cluster cardinality.
assert cardinality >= minBaseClusterSize;
/*
* Consider certain special cases of internal suffix tree nodes.
*/
if (!checkAcceptablePhrase(path))
{
return;
}
// Calculate "effective phrase length", which is the number of non-stopwords.
final int effectivePhraseLen = effectivePhraseLength(path);
if (effectivePhraseLen == 0)
{
return;
}
/*
* Calculate base cluster's score as a function of effective phrase's length.
* STC originally used a linear gradient, we modified it to penalize very long
* phrases (which usually correspond to duplicated snippets anyway).
*/
final float score = baseClusterScore(effectivePhraseLen, cardinality);
candidates.add(
new ClusterCandidate(path.toArray(),
(BitSet) documents.clone(), cardinality, score));
}
}.visit();
/*
* Combine all phrases that are stem-equivalent into one candidate.
*/
if (mergeStemEquivalentBaseClusters)
{
mergeStemEquivalentBaseClusters(sb, candidates);
}
/*
* Remove any base clusters that fall below the minimum score.
*/
int j = 0;
for (int max = candidates.size(), i = 0; i < max; i++)
{
ClusterCandidate cc = candidates.get(i);
if (cc.score >= minBaseClusterScore) {
candidates.set(j++, cc);
}
}
candidates.subList(j, candidates.size()).clear();
/*
* We limit the number of base clusters to the one requested by the user.
* First we sort by the base clusters score, then pick the top-K entries,
* filtering out any stop labels on the way.
*/
Collections.sort(candidates, new Comparator()
{
@Override
public int compare(ClusterCandidate c1, ClusterCandidate c2)
{
return -Float.compare(c1.score, c2.score);
}
});
j = 0;
ILexicalData lexicalData = context.language.getLexicalData();
for (int max = candidates.size(), i = 0; i < max && j < maxBaseClusters; i++)
{
ClusterCandidate cc = candidates.get(i);
// Build the candidate cluster's label for filtering. This may be costly so
// we only do this for base clusters which are promoted to merging phase.
assert cc.phrases.size() == 1;
if (!lexicalData.isStopLabel(buildLabel(cc.phrases.get(0))))
{
candidates.set(j++, cc);
}
}
if (j < candidates.size())
{
candidates.subList(j, candidates.size()).clear();
assert candidates.size() == j;
}
return candidates;
}
/* */
private void mergeStemEquivalentBaseClusters(SequenceBuilder sb, final List candidates)
{
// Look for candidates to merge.
Map merged = Maps.newHashMap();
int j = 0;
for (int max = candidates.size(), i = 0; i < max; i++)
{
ClusterCandidate cc = candidates.get(i);
candidates.set(j, cc);
// Convert word indices to stem indices.
assert cc.phrases.size() == 1;
int [] stemIndices = context.allWords.stemIndex;
int [] phraseWords = cc.phrases.get(0);
IntArrayList stemList = new IntArrayList(phraseWords.length);
for (int seqIndex : phraseWords)
{
int termIndex = sb.input.get(seqIndex);
stemList.add(stemIndices[termIndex]);
}
// Check if we have stem-equivalent phrase like this.
ClusterCandidate equivalent = merged.get(stemList);
if (equivalent == null)
{
merged.put(stemList, cc);
j++;
}
else
{
// Merge the two candidates. The surface form with the highest cardinality
// is taken as the representation of an equivalence group.
if (equivalent.cardinality < cc.cardinality)
{
equivalent.cardinality = cc.cardinality;
equivalent.phrases.add(0, cc.phrases.get(0));
}
else
{
equivalent.phrases.add(cc.phrases.get(0));
}
// Collect actual documents to recompute cardinality later on.
equivalent.documents.or(cc.documents);
}
}
// Trim to only include shifted merged candidates.
candidates.subList(j, candidates.size()).clear();
// Recalculate score after merging.
IntStack scratch = new IntStack();
for (ClusterCandidate cc : candidates)
{
if (cc.phrases.size() > 1)
{
cc.cardinality = (int) cc.documents.cardinality();
scratch.buffer = cc.phrases.get(0);
scratch.elementsCount = scratch.buffer.length;
cc.score = baseClusterScore(
effectivePhraseLength(scratch),
cc.cardinality);
// Clear any other phrase variants.
cc.phrases.subList(1, cc.phrases.size()).clear();
}
}
}
/**
* Create final clusters by merging base clusters and pruning their labels. Cluster
* merging is a greedy process of compacting clusters with document sets that overlap
* by a certain ratio. In other words, phrases that "cover" nearly identical document
* sets will be conflated.
*/
private ArrayList createMergedClusters(List baseClusters)
{
/*
* Calculate overlap between base clusters first, saving adjacency lists for
* each base cluster.
*/
// [i] - next neighbor or END, [i + 1] - neighbor cluster index.
final int END = -1;
final IntStack neighborList = new IntStack();
neighborList.push(END);
final int [] neighbors = new int [baseClusters.size()];
final float m = (float) mergeThreshold;
for (int i = 0; i < baseClusters.size(); i++)
{
for (int j = i + 1; j < baseClusters.size(); j++)
{
final ClusterCandidate c1 = baseClusters.get(i);
final ClusterCandidate c2 = baseClusters.get(j);
final float a = c1.cardinality;
final float b = c2.cardinality;
final float c = BitSet.intersectionCount(c1.documents, c2.documents);
if (c / a > m && c / b > m)
{
neighborList.push(neighbors[i], j);
neighbors[i] = neighborList.size() - 2;
neighborList.push(neighbors[j], i);
neighbors[j] = neighborList.size() - 2;
}
}
}
/*
* Find connected components in the similarity graph using Tarjan's algorithm
* (flattened to use the stack instead of recursion).
*/
final int NO_INDEX = -1;
final int [] merged = new int [baseClusters.size()];
Arrays.fill(merged, NO_INDEX);
final ArrayList mergedClusters =
Lists.newArrayListWithCapacity(baseClusters.size());
final IntStack stack = new IntStack(baseClusters.size());
final IntStack mergeList = new IntStack(baseClusters.size());
int mergedIndex = 0;
for (int v = 0; v < baseClusters.size(); v++)
{
if (merged[v] != NO_INDEX) continue;
// Recursively mark all connected components from an unmerged cluster.
stack.push(v);
while (stack.size() > 0)
{
final int c = stack.pop();
assert merged[c] == NO_INDEX || merged[c] == mergedIndex;
if (merged[c] == mergedIndex) continue;
merged[c] = mergedIndex;
mergeList.push(c);
for (int i = neighbors[c]; neighborList.get(i) != END;)
{
final int neighbor = neighborList.get(i + 1);
if (merged[neighbor] == NO_INDEX)
{
stack.push(neighbor);
}
else
{
assert merged[neighbor] == mergedIndex;
}
i = neighborList.get(i);
}
}
mergedIndex++;
/*
* Aggregate documents from each base cluster of the current merge, compute
* the score and labels.
*/
mergedClusters.add(merge(mergeList, baseClusters));
mergeList.clear();
}
/*
* Sort merged clusters.
*/
Collections.sort(mergedClusters, new Comparator() {
public int compare(ClusterCandidate c1, ClusterCandidate c2) {
if (c1.score < c2.score) return 1;
if (c1.score > c2.score) return -1;
if (c1.cardinality < c2.cardinality) return 1;
if (c1.cardinality > c2.cardinality) return -1;
return 0;
};
});
if (mergedClusters.size() > maxClusters)
{
mergedClusters.subList(maxClusters, mergedClusters.size()).clear();
}
return mergedClusters;
}
/**
* Merge a list of base clusters into one.
*/
private ClusterCandidate merge(IntStack mergeList,
List baseClusters)
{
assert mergeList.size() > 0;
final ClusterCandidate result = new ClusterCandidate();
/*
* Merge documents from all base clusters and update the score.
*/
for (int i = 0; i < mergeList.size(); i++)
{
final ClusterCandidate cc = baseClusters.get(mergeList.get(i));
result.documents.or(cc.documents);
result.score += cc.score;
}
result.cardinality = (int) result.documents.cardinality();
/*
* Combine cluster labels and try to find the best description for the cluster.
*/
final ArrayList phrases =
new ArrayList(mergeList.size());
for (int i = 0; i < mergeList.size(); i++)
{
final ClusterCandidate cc = baseClusters.get(mergeList.get(i));
final float coverage = cc.cardinality / (float) result.cardinality;
phrases.add(new PhraseCandidate(cc, coverage));
}
markSubSuperPhrases(phrases);
Collections2.filter(phrases, notSelected).clear();
markOverlappingPhrases(phrases);
Collections2.filter(phrases, notSelected).clear();
Collections.sort(phrases, new Comparator() {
public int compare(PhraseCandidate p1, PhraseCandidate p2) {
if (p1.coverage < p2.coverage) return 1;
if (p1.coverage > p2.coverage) return -1;
return 0;
};
});
int max = maxPhrases;
for (PhraseCandidate p : phrases)
{
if (max-- <= 0) break;
result.phrases.add(p.cluster.phrases.get(0));
}
return result;
}
/**
* Leave only most general (no other phrase is a substring of this one) and
* most specific (no other phrase is a superstring of this one) phrases.
*/
private void markSubSuperPhrases(ArrayList phrases)
{
final int max = phrases.size();
// A list of all words for each candidate phrase.
final IntStack words = new IntStack(
maxDescPhraseLength * phrases.size());
// Offset pairs in the words list -- a pair [start, length].
final IntStack offsets = new IntStack(phrases.size() * 2);
for (PhraseCandidate p : phrases)
{
appendWords(words, offsets, p);
}
/*
* Mark phrases that cannot be most specific or most general.
*/
for (int i = 0; i < max; i++)
{
for (int j = 0; j < max; j++)
{
if (i == j) continue;
int index = indexOf(
words.buffer, offsets.get(2 * i), offsets.get(2 * i + 1),
words.buffer, offsets.get(2 * j), offsets.get(2 * j + 1));
if (index >= 0)
{
// j is a subphrase of i, hence i cannot be mostGeneral and j
// cannot be most specific.
phrases.get(i).mostGeneral = false;
phrases.get(j).mostSpecific = false;
}
}
}
/*
* For most general phrases, do not display them if a more specific phrase
* exists with pretty much the same coverage.
*/
for (int i = 0; i < max; i++)
{
final PhraseCandidate a = phrases.get(i);
if (!a.mostGeneral) continue;
for (int j = 0; j < max; j++)
{
final PhraseCandidate b = phrases.get(j);
if (i == j || !b.mostSpecific) continue;
int index = indexOf(
words.buffer, offsets.get(2 * j), offsets.get(2 * j + 1),
words.buffer, offsets.get(2 * i), offsets.get(2 * i + 1));
if (index >= 0)
{
if (a.coverage - b.coverage < mostGeneralPhraseCoverage)
{
a.selected = false;
j = max;
}
}
}
}
/*
* Mark phrases that should be removed from the candidate set.
*/
for (PhraseCandidate p : phrases)
{
if (!p.mostGeneral && !p.mostSpecific)
{
p.selected = false;
}
}
}
/**
* Mark those phrases that overlap with other phrases by more than
* {@link #maxPhraseOverlap} and have lower coverage.
*/
private void markOverlappingPhrases(ArrayList phrases)
{
final int max = phrases.size();
// A list of all unique words for each candidate phrase.
final IntStack words = new IntStack(
maxDescPhraseLength * phrases.size());
// Offset pairs in the words list -- a pair [start, length].
final IntStack offsets = new IntStack(phrases.size() * 2);
for (PhraseCandidate p : phrases)
{
appendUniqueWords(words, offsets, p);
}
for (int i = 0; i < max; i++)
{
for (int j = i + 1; j < max; j++)
{
final PhraseCandidate a = phrases.get(i);
final PhraseCandidate b = phrases.get(j);
final int a_words = offsets.get(2 * i + 1);
final int b_words = offsets.get(2 * j + 1);
final float intersection = computeIntersection(
words.buffer, offsets.get(2 * i), a_words,
words.buffer, offsets.get(2 * j), b_words);
if ((intersection / b_words) > maxPhraseOverlap
&& b.coverage < a.coverage)
{
b.selected = false;
}
if ((intersection / a_words) > maxPhraseOverlap
&& a.coverage < b.coverage)
{
a.selected = false;
}
}
}
}
/**
* Compute the number of common elements in two (sorted) lists.
*/
static int computeIntersection(int [] a, int aPos, int aLength, int [] b, int bPos, int bLength)
{
final int maxa = aPos + aLength;
final int maxb = bPos + bLength;
int ea;
int eb;
int common = 0;
while (aPos < maxa && bPos < maxb)
{
ea = a[aPos]; eb = b[bPos];
if (ea >= eb) bPos++;
if (ea <= eb) aPos++;
if (ea == eb) common++;
}
return common;
}
/**
* Collect all unique non-stop word from a phrase.
*/
private void appendUniqueWords(IntStack words, IntStack offsets, PhraseCandidate p)
{
assert p.cluster.phrases.size() == 1;
final int start = words.size();
final int [] phraseIndices = p.cluster.phrases.get(0);
final short [] tokenTypes = context.allWords.type;
for (int i = 0; i < phraseIndices.length; i += 2)
{
for (int j = phraseIndices[i]; j <= phraseIndices[i + 1]; j++)
{
final int termIndex = sb.input.get(j);
if (!TokenTypeUtils.isCommon(tokenTypes[termIndex]))
{
words.push(termIndex);
}
}
}
// Sort words, we don't care about their order when counting subsets.
Arrays.sort(words.buffer, start, words.size());
// Reorder to keep only unique words.
int j = start;
for (int i = start + 1; i < words.size(); i++)
{
if (words.buffer[j] != words.buffer[i])
{
words.buffer[++j] = words.buffer[i];
}
}
words.elementsCount = j + 1;
offsets.push(start, words.size() - start);
}
/**
* Collect all words from a phrase.
*/
private void appendWords(IntStack words, IntStack offsets, PhraseCandidate p)
{
final int start = words.size();
final int [] phraseIndices = p.cluster.phrases.get(0);
final short [] tokenTypes = context.allWords.type;
for (int i = 0; i < phraseIndices.length; i += 2)
{
for (int j = phraseIndices[i]; j <= phraseIndices[i + 1]; j++)
{
final int termIndex = sb.input.get(j);
if (!TokenTypeUtils.isCommon(tokenTypes[termIndex]))
{
words.push(termIndex);
}
}
}
offsets.push(start, words.size() - start);
}
/**
* Create the junk (unassigned documents) cluster and create the final
* set of clusters in Carrot2 format.
*/
private void postProcessing(List clusters)
{
// Adapt to Carrot2 classes, counting used documents on the way.
final BitSet all = new BitSet(documents.size());
final ArrayList docs = Lists.newArrayListWithCapacity(documents.size());
final ArrayList phrases = Lists.newArrayListWithCapacity(3);
for (ClusterCandidate c : clusters)
{
final Cluster c2 = new Cluster();
c2.addPhrases(collectPhrases(phrases, c));
c2.addDocuments(collectDocuments(docs, c.documents));
c2.setScore((double) c.score);
this.clusters.add(c2);
all.or(c.documents);
docs.clear();
phrases.clear();
}
Collections.sort(this.clusters,
Cluster.byReversedWeightedScoreAndSizeComparator(scoreWeight));
Cluster.appendOtherTopics(this.documents, this.clusters);
}
/**
* Collect phrases from a cluster.
*/
private List collectPhrases(List l, ClusterCandidate c)
{
assert l != null;
for (int [] phraseIndexes : c.phrases)
{
l.add(buildLabel(phraseIndexes));
}
return l;
}
/**
* Collect documents from a bitset.
*/
private List collectDocuments(List l, BitSet bitset)
{
if (l == null)
{
l = Lists.newArrayListWithCapacity((int) bitset.cardinality());
}
final BitSetIterator i = bitset.iterator();
for (int d = i.nextSetBit(); d >= 0; d = i.nextSetBit())
{
l.add(documents.get(d));
}
return l;
}
/**
* Build the cluster's label from suffix tree edge indices.
*/
private String buildLabel(int [] phraseIndices)
{
// Count the number of terms first.
int termsCount = 0;
for (int j = 0; j < phraseIndices.length; j += 2)
{
termsCount += phraseIndices[j + 1] - phraseIndices[j] + 1;
}
// Extract terms info for the phrase and construct the label.
final boolean [] stopwords = new boolean[termsCount];
final char [][] images = new char [termsCount][];
final short [] tokenTypes = context.allWords.type;
int k = 0;
for (int i = 0; i < phraseIndices.length; i += 2)
{
for (int j = phraseIndices[i]; j <= phraseIndices[i + 1]; j++, k++)
{
final int termIndex = sb.input.get(j);
images[k] = context.allWords.image[termIndex];
stopwords[k] = TokenTypeUtils.isCommon(tokenTypes[termIndex]);
}
}
return LabelFormatter.format(images, stopwords,
context.language.getLanguageCode().usesSpaceDelimiters());
}
@SuppressWarnings("unused")
private String toString(PhraseCandidate c)
{
return String.format(Locale.ENGLISH, "%3.2f %s %s %s %s",
c.coverage,
buildLabel(c.cluster.phrases.get(0)),
c.selected ? "S" : "",
c.mostGeneral ? "MG" : "",
c.mostSpecific ? "MS" : "");
}
/**
* Build a cluster's label from suffix tree edge indices, including some debugging and
* diagnostic information.
*/
@SuppressWarnings("unused")
private String buildDebugLabel(int [] phraseIndices)
{
final StringBuilder b = new StringBuilder();
String sep = "";
int k = 0;
final short [] tokenTypes = context.allWords.type;
for (int i = 0; i < phraseIndices.length; i += 2)
{
for (int j = phraseIndices[i]; j <= phraseIndices[i + 1]; j++, k++)
{
b.append(sep);
final int termIndex = sb.input.get(j);
b.append(context.allWords.image[termIndex]);
if (TokenTypeUtils.isCommon(tokenTypes[termIndex])) b.append("[S]");
sep = " ";
}
sep = "_";
}
return b.toString();
}
/**
* Consider certain special cases of internal suffix tree nodes. The suffix tree may
* contain internal nodes with paths starting or ending with a stop word (common
* word). We have the following interesting scenarios:
*
*
* - IF LEADING STOPWORD: IGNORE THE NODE.
* -
* There MUST be a phrase with this stopword chopped off in the suffix tree
* (a suffix of this phrase) and its frequency will be just as high.
*
* - IF TRAILING STOPWORDS:
* -
* Check if the edge leading to the current node is composed entirely of stopwords. If so,
* there must be a parent node that contains non-stopwords and we can ignore the current node.
* Otherwise we can chop off the trailing stopwords from the current node's phrase (this phrase
* cannot be duplicated anywhere in the tree because if it were, there would have to be a branch
* somewhere in the suffix tree on the edge).
*
*/
final boolean checkAcceptablePhrase(IntStack path)
{
assert path.size() > 0;
final int [] terms = sb.input.buffer;
final short [] tokenTypes = context.allWords.type;
// Ignore nodes that start with a stop word.
if (TokenTypeUtils.isCommon(tokenTypes[terms[path.get(0)]]))
{
return false;
}
// Check the last edge of the current node.
int i = path.get(path.size() - 2);
int j = path.get(path.size() - 1);
final int k = j;
while (i <= j && TokenTypeUtils.isCommon(tokenTypes[terms[j]]))
{
j--;
}
if (j < i)
{
// If the edge contains only stopwords, ignore the node.
return false;
}
else if (j < k)
{
// There have been trailing stop words on the edge. Chop them off.
path.buffer[path.size() - 1] = j;
}
// Check the total phrase length (in words, including stopwords).
int termsCount = 0;
for (j = 0; j < path.size(); j += 2)
{
termsCount += path.get(j + 1) - path.get(j) + 1;
}
if (termsCount > maxDescPhraseLength)
{
return false;
}
return true;
}
/**
* Calculate "effective phrase length", that is the number of non-ignored words
* in the phrase.
*/
final int effectivePhraseLength(IntStack path)
{
final int [] terms = sb.input.buffer;
final int lower = ignoreWordIfInFewerDocs;
final int upper = (int) (ignoreWordIfInHigherDocsPercent * documents.size());
int effectivePhraseLen = 0;
for (int i = 0; i < path.size(); i += 2)
{
for (int j = path.get(i); j <= path.get(i + 1); j++)
{
final int termIndex = terms[j];
// If this term is a stop word, don't count it.
if (TokenTypeUtils.isCommon(context.allWords.type[termIndex]))
{
continue;
}
// If this word occurs in more than a given fraction of the input
// collection don't count it.
final int docCount = context.allWords.tfByDocument[termIndex].length / 2;
if (docCount < lower || docCount > upper)
{
continue;
}
effectivePhraseLen++;
}
}
return effectivePhraseLen;
}
/**
* Calculates base cluster score.
*
* The boost is calculated as a Gaussian function of density around the "optimum"
* expected phrase length (average) and "tolerance" towards shorter and longer phrases
* (standard deviation). You can draw this score multiplier's characteristic with
* gnuplot:
*
* reset
*
* set xrange [0:10]
* set yrange [0:]
* set samples 11
* set boxwidth 1 absolute
*
* set xlabel "Phrase length"
* set ylabel "Score multiplier"
*
* set border 3
* set key noautotitles
*
* set grid
*
* set xtics border nomirror 1
* set ytics border nomirror
* set ticscale 1.0
* show tics
*
* set size ratio .5
*
* # Base cluster boost function.
* boost(x) = exp(-(x - optimal) * (x - optimal) / (2 * tolerance * tolerance))
*
* plot optimal=2, tolerance=2, boost(x) with histeps title "optimal=2, tolerance=2", \
* optimal=2, tolerance=4, boost(x) with histeps title "optimal=2, tolerance=4", \
* optimal=2, tolerance=6, boost(x) with histeps title "optimal=2, tolerance=6"
*
* pause -1
*
* One word-phrases can be given a fixed boost, if
* {@link #singleTermBoost} is greater than zero.
*
* @param phraseLength Effective phrase length (number of non-stopwords).
* @param documentCount Number of documents this phrase occurred in.
* @return Returns the base cluster score calculated as a function of the number of
* documents the phrase occurred in and a function of the effective length of
* the phrase.
*/
final float baseClusterScore(final int phraseLength, final int documentCount)
{
final double boost;
if (phraseLength == 1 && singleTermBoost > 0)
{
boost = singleTermBoost;
}
else
{
final int tmp = phraseLength - optimalPhraseLength;
boost = Math.exp((-tmp * tmp)
/ (2 * optimalPhraseLengthDev * optimalPhraseLengthDev));
}
return (float) (boost * (documentCount * documentCountBoost));
}
/**
* Subsequence search in int arrays.
*/
private static int indexOf(int [] source, int sourceOffset, int sourceCount,
int [] target, int targetOffset, int targetCount)
{
if (targetCount == 0)
{
return 0;
}
final int first = target[targetOffset];
final int max = sourceOffset + (sourceCount - targetCount);
for (int i = sourceOffset; i <= max; i++)
{
/* Look for first element. */
if (source[i] != first)
{
while (++i <= max && source[i] != first) /* do nothing */;
}
/* Found first element, now look at the rest of the pattern */
if (i <= max)
{
int j = i + 1;
int end = j + targetCount - 1;
for (int k = targetOffset + 1; j < end && source[j] == target[k]; j++, k++)
/* do nothing */;
if (j == end)
{
/* Found whole pattern. */
return i - sourceOffset;
}
}
}
return -1;
}
}