/** 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);
}
/** Merge a list of base clusters into one. */
private ClusterCandidate merge(IntStack mergeList, List<ClusterCandidate> 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<PhraseCandidate> phrases = new ArrayList<PhraseCandidate>(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<PhraseCandidate>() {
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;
}
/** 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;
}
/** 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);
}
/**
* 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:
*
* <dl>
* <dt>IF LEADING STOPWORD: IGNORE THE NODE.
* <dd>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.
* <dt>IF TRAILING STOPWORDS:
* <dd>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).
* </dl>
*/
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;
}
/**
* 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<ClusterCandidate> createMergedClusters(
ArrayList<ClusterCandidate> 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<ClusterCandidate> 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<ClusterCandidate>() {
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;
}