/**
* Get all of the words in the evaluation dataset.
*
* @param path
* @return
* @throws Exception
*/
private static Set<String> getWordSimVocab(String path) throws Exception {
Set<String> vocab = new HashSet<String>();
BufferedReader reader = new BufferedReader(new FileReader(path));
String line = "";
line = reader.readLine();
String[] keys = line.split(",");
// Read the first line that contains the column keys.
if (keys.length != 3) {
System.out.println(
"There should be two words per line "
+ "and a single score for each of these word "
+ "pairs. We just saw, "
+ line);
System.exit(0);
}
while ((line = reader.readLine()) != null) {
String[] parts = line.split(",");
if (parts.length != 3) {
System.out.println("WordSim line: " + line + " should contain two words and a score.");
System.exit(0);
}
String word1 = parts[0];
String word2 = parts[1];
vocab.add(word1);
vocab.add(word2);
}
reader.close();
return vocab;
}
private Set<Integer> negativeSampleContexts(int wordIndex) {
Set<Integer> negativeContexts = new HashSet<Integer>();
Set<Integer> positiveContexts = contextPairs.get(wordIndex);
while (negativeContexts.size() < kSamples) {
int contextIndex = (int) (Math.random() * V);
if (!positiveContexts.contains(contextIndex) && negativeContexts.contains(contextIndex)) {
negativeContexts.add(contextIndex);
}
}
return negativeContexts;
}
/**
* @author jacqueline If boolean sampleUnigram = true, we use noiseSampler from
* randomContextGeneration to model the unigram probability distribution raised to specfied
* power, default 3/4. Otherwise, use overloaded negativeSampleContexts(int wordIndex)
* method to draw from uniform probability distribution.
*/
private Set<Integer> negativeSampleContexts(
int wordIndex, EnumeratedDistribution<String> weightedRandomSample) {
Set<Integer> negativeContexts = new HashSet<Integer>();
Set<Integer> positiveContexts = contextPairs.get(wordIndex);
while (negativeContexts.size() < kSamples) {
String possibleContext = weightedRandomSample.sample();
int contextIndex = encodedVocab.get(possibleContext);
if (!positiveContexts.contains(contextIndex) && !negativeContexts.contains(contextIndex)) {
negativeContexts.add(contextIndex);
}
}
return negativeContexts;
}
private Pair<Integer, Set<Integer>> getWordContextPair(
List<String> sentence, int wordPosition) {
String centerWord = sentence.get(wordPosition);
int centerWordIndex = encodedVocab.get(centerWord);
Set<Integer> contextWordSet = new HashSet<Integer>();
for (int i = wordPosition - contextSize; i < wordPosition + contextSize; i++) {
if (i < 0) continue; // Ignore contexts prior to start of sentence
if (i >= sentence.size()) break; // Ignore contexts after end of current sentence
if (i == centerWordIndex) continue; // Ignore center word
String contextWord = sentence.get(i);
int contextWordIndex = encodedVocab.get(contextWord);
contextWordSet.add(contextWordIndex);
}
return Pair.create(centerWordIndex, contextWordSet);
}
