private void setAllContexts() {
this.contextPairs = new HashMap<Integer, Set<Integer>>();
for (int wordIndex : encodedVocab.values()) {
contextPairs.put(wordIndex, new HashSet<Integer>());
}
for (List<String> sentence : sentenceCollection) {
for (int wordPosition = 0; wordPosition < sentence.size(); wordPosition++) {
Pair<Integer, Set<Integer>> wordPlusContext = getWordContextPair(sentence, wordPosition);
int wordIndex = wordPlusContext.getFirst();
(contextPairs.get(wordIndex)).addAll(wordPlusContext.getSecond());
}
}
}
public static void main(String[] args) throws Exception {
// Parse command line flags and arguments.
Map<String, String> argMap = CommandLineUtils.simpleCommandLineParser(args);
// Read commandline parameters.
String embeddingPath = "";
if (!argMap.containsKey("-embeddings")) {
System.out.println("-embeddings flag required.");
System.exit(0);
} else {
embeddingPath = argMap.get("-embeddings");
}
String wordSimPath = "";
if (!argMap.containsKey("-wordsim")) {
System.out.println("-wordsim flag required.");
System.exit(0);
} else {
wordSimPath = argMap.get("-wordsim");
}
// Read in the labeled similarities and generate the target vocabulary.
System.out.println("Loading wordsim353 ...");
List<Pair<Pair<String, String>, Float>> wordSimPairs = readWordSimPairs(wordSimPath);
Set<String> targetVocab = getWordSimVocab(wordSimPath);
// It is likely that you will want to generate your embeddings
// elsewhere. But this supports the option to generate the embeddings
// and evaluate them in a single loop.
HashMap<String, float[]> embeddings;
if (argMap.containsKey("-trainandeval")) {
// Get some training data.
String dataPath = "";
if (!argMap.containsKey("-trainingdata")) {
System.out.println("-trainingdata flag required with -trainandeval");
System.exit(0);
} else {
dataPath = argMap.get("-trainingdata");
}
// Since this simple approach does not do dimensionality reduction
// on the co-occurrence vectors, we instead control the size of the
// vectors by only counting co-occurrence with core WordNet senses.
String wordNetPath = "";
if (!argMap.containsKey("-wordnetdata")) {
System.out.println("-wordnetdata flag required with -trainandeval");
System.exit(0);
} else {
wordNetPath = argMap.get("-wordnetdata");
}
// HashMap<String, Integer> contentWordVocab = getWordNetVocab(wordNetPath);
System.out.println("Training embeddings on " + dataPath + " ...");
// embeddings = getEmbeddings(dataPath, contentWordVocab, targetVocab);
int kSamples = 5;
int dimensions = 100;
int contextSize = 2;
WordSim skipgram = new WordSim(dataPath, kSamples, dimensions, contextSize);
embeddings = skipgram.getEmbeddings(targetVocab);
// Keep only the words that are needed.
System.out.println("Writing embeddings to " + embeddingPath + " ...");
// embeddings = reduceVocabulary(embeddings, targetVocab);
// writeEmbeddings(embeddings, embeddingPath, contentVocab.size());
writeEmbeddings(embeddings, embeddingPath, dimensions);
} else {
// Read in embeddings.
System.out.println("Loading embeddings ...");
embeddings = readEmbeddings(embeddingPath);
// Keep only the words that are needed.
System.out.println(
"Writing reduced vocabulary embeddings to " + embeddingPath + ".reduced ...");
embeddings = reduceVocabulary(embeddings, targetVocab);
writeEmbeddings(
embeddings, embeddingPath + ".reduced", embeddings.values().iterator().next().length);
}
reduceVocabulary(embeddings, targetVocab);
double score = spearmansScore(wordSimPairs, embeddings);
System.out.println("Score is " + score);
}
