public static ArrayList<TaggedWord> StopWordRemoval(ArrayList<TaggedWord> taggedWords) {
ArrayList<TaggedWord> newList = new ArrayList<TaggedWord>();
try {
String path = "data/nltk_stoplist.txt";
File textFile = new File(path);
BufferedReader br = new BufferedReader(new FileReader(textFile));
String stopwordsLine = br.readLine();
br.close();
String[] stopwords = stopwordsLine.split(",");
HashMap<String, String> stopwordsDict = new HashMap<String, String>();
for (int i = 0; i < stopwords.length; i++) {
stopwordsDict.put(stopwords[i], stopwords[i]);
}
for (int i = 0; i < taggedWords.size(); i++) {
String word = taggedWords.get(i).word();
String posTag = taggedWords.get(i).tag();
if (!stopwordsDict.containsKey(word.toLowerCase())) {
String newWord, newPosTag;
newWord = word;
newPosTag = posTag;
newList.add(new TaggedWord(newWord, newPosTag));
}
}
} catch (Exception ex) {
ex.printStackTrace();
}
return newList;
}
public static ArrayList<ArrayList<TaggedWord>> getPhrasesNaive(
String sentence, LexicalizedParser lp, AbstractSequenceClassifier<CoreLabel> classifier) {
ArrayList<ArrayList<TaggedWord>> newList = new ArrayList<ArrayList<TaggedWord>>();
ArrayList<TaggedWord> taggedWords = StanfordNER.parse(sentence, lp, classifier);
HashMap<String, String> phraseBoundaries = new HashMap<String, String>();
phraseBoundaries.put(",", ",");
phraseBoundaries.put("\"", "\"");
phraseBoundaries.put("''", "''");
phraseBoundaries.put("``", "``");
phraseBoundaries.put("--", "--");
// List<Tree> leaves = parse.getLeaves();
ArrayList<TaggedWord> temp = new ArrayList<TaggedWord>();
int index = 0;
while (index < taggedWords.size()) {
if ((phraseBoundaries.containsKey(taggedWords.get(index).word()))) {
if (temp.size() > 0) {
// System.out.println(temp);
ArrayList<TaggedWord> tempCopy = new ArrayList<TaggedWord>(temp);
newList.add(Preprocess(tempCopy));
}
temp.clear();
} else {
// System.out.println(taggedWords.get(index).toString());
temp.add(taggedWords.get(index));
}
index += 1;
}
if (temp.size() > 0) {
ArrayList<TaggedWord> tempCopy = new ArrayList<TaggedWord>(temp);
newList.add(Preprocess(tempCopy));
}
// System.out.println(newList);
return newList;
}
public ArrayList<String> getNounsFromSentence(String sentence) {
ArrayList<TaggedWord> tw = parseSentenceTD(sentence);
ArrayList<String> nouns = new ArrayList<String>();
for (TaggedWord t : tw) {
if (t.tag().startsWith("N")) {
nouns.add(t.value());
}
}
return nouns;
}
public static double LexicalSimilarityScoreMin(
ArrayList<TaggedWord> taggedWords1,
ArrayList<TaggedWord> taggedWords2,
DISCOSimilarity discoRAM,
LexicalizedParser lp) {
// System.out.println(taggedWords1.size() + "," + taggedWords2.size());
// array of edge weights with default weight 0
int length1 = taggedWords1.size();
int length2 = taggedWords2.size();
int arrSize = Math.max(length1, length2);
double[][] array = new double[arrSize][arrSize];
for (int i = 0; i < arrSize; i++) {
for (int j = 0; j < arrSize; j++) {
array[i][j] = 0;
}
}
for (int i = 0; i < length1; i++) {
for (int j = 0; j < length2; j++) {
String word1 = taggedWords1.get(i).word();
String word2 = taggedWords2.get(j).word();
double edgeWeight = 0;
// LSA Similarity
// edgeWeight = LSASimilarity.LSAWordSimilarity(word1, word2);
// DISCO Similarity
// DISCOSimilarity discoObj = new DISCOSimilarity();
try {
if (word1.compareToIgnoreCase(word2) == 0) edgeWeight = 1;
else {
edgeWeight = discoRAM.similarity2(word1, word2);
// edgeWeight = LSASimilarity.LSAWordSimilarity(word1, word2);
}
} catch (Exception ex) {
ex.printStackTrace();
}
array[i][j] = edgeWeight;
}
}
// System.out.println("Hungarian starts " + arrSize);
double finalScore;
String sumType = "max";
int minLength = Math.min(length1, length2);
finalScore = HungarianAlgorithm.hgAlgorithm(array, sumType) / minLength * 5;
// finalScore = HungarianAlgorithm.hgAlgorithm(array, sumType)/arrSize * 5;
return finalScore;
}
public ArrayList<String> getPairsFromSentence(String sentence) {
Collection<TypedDependency> tdl = parseSentenceTDL(sentence);
ArrayList<String> pairs = new ArrayList<String>();
for (TypedDependency td : tdl) {
StringBuilder sb = new StringBuilder();
sb.append(td.gov().originalText());
sb.append(" ");
sb.append(td.dep().originalText());
pairs.add(sb.toString());
}
return pairs;
}
public static ArrayList<TaggedWord> PreprocessPhrase(ArrayList<TaggedWord> taggedWords) {
ArrayList<TaggedWord> newList = new ArrayList<TaggedWord>();
taggedWords = Preprocess(taggedWords);
for (int i = 0; i < taggedWords.size(); i++) {
String posTag = taggedWords.get(i).tag();
if (posTag.length() >= 2) {
String reducedTag = posTag.substring(0, 2);
if (reducedTag.equals("CD") || reducedTag.equals("NN") || reducedTag.equals("VB")) {
newList.add(taggedWords.get(i));
}
}
}
return newList;
}
public static ArrayList<ArrayList<TaggedWord>> getPhrases(Tree parse, int phraseSizeLimit) {
ArrayList<ArrayList<TaggedWord>> newList = new ArrayList<ArrayList<TaggedWord>>();
List<Tree> leaves = parse.getLeaves();
if (leaves.size() <= phraseSizeLimit) {
// ArrayList<TaggedWord> phraseElements = PreprocessPhrase(parse.taggedYield());
ArrayList<TaggedWord> phraseElements = Preprocess(parse.taggedYield());
if (phraseElements.size() > 0) newList.add(phraseElements);
} else {
Tree[] childrenNodes = parse.children();
for (int i = 0; i < childrenNodes.length; i++) {
Tree currentParse = childrenNodes[i];
newList.addAll(getPhrases(currentParse, phraseSizeLimit));
}
}
return newList;
}
public static void main(String args[]) {
// String sentence1 = "A large bird standing on a table picks up a plastic glass
// containing liquid and places it in a bowl of something.";
// String sentence2 = "A bird picks up a plastic cup containing a liquid with it's beak
// and puts the cup into a bowl.";
// LexicalizedParser lp = new LexicalizedParser("englishPCFG.ser.gz");
// LeskWSD tm = new LeskWSD(lp);
// WordNetSimilarity ws = new WordNetSimilarity();
//
// System.out.println(LexicalSimilarityScoreWordNet(sentence1, sentence2, tm, lp, ws));
String sentence =
"The broader Standard & Poor's 500 Index <.SPX> shed 2.38 points, or 0.24 percent, at 995.10.";
LexicalizedParser lp = new LexicalizedParser("englishPCFG.ser.gz");
Tree parse = lp.apply(sentence);
ArrayList<TaggedWord> taggedWords = parse.taggedYield();
taggedWords = Preprocess(taggedWords);
for (int i = 0; i < taggedWords.size(); i++) System.out.println(taggedWords.get(i).word());
}
public static double BestWordMatchEdgeWeight(
ArrayList<TaggedWord> taggedWords1,
ArrayList<TaggedWord> taggedWords2,
DISCOSimilarity discoRAM) {
double bestMatchScore = 0;
for (int i = 0; i < taggedWords1.size(); i++) {
String word1 = taggedWords1.get(i).word();
for (int j = 0; j < taggedWords2.size(); j++) {
String word2 = taggedWords2.get(j).word();
double currentScore;
if (word1.equals(word2)) currentScore = 1;
else currentScore = discoRAM.similarity2(word1, word2);
if (currentScore > bestMatchScore) bestMatchScore = currentScore;
}
}
return bestMatchScore;
}
private Distribution<Integer> getSegmentedWordLengthDistribution(Treebank tb) {
// CharacterLevelTagExtender ext = new CharacterLevelTagExtender();
ClassicCounter<Integer> c = new ClassicCounter<Integer>();
for (Iterator iterator = tb.iterator(); iterator.hasNext(); ) {
Tree gold = (Tree) iterator.next();
StringBuilder goldChars = new StringBuilder();
ArrayList goldYield = gold.yield();
for (Iterator wordIter = goldYield.iterator(); wordIter.hasNext(); ) {
Word word = (Word) wordIter.next();
goldChars.append(word);
}
List<HasWord> ourWords = segment(goldChars.toString());
for (int i = 0; i < ourWords.size(); i++) {
c.incrementCount(Integer.valueOf(ourWords.get(i).word().length()));
}
}
return Distribution.getDistribution(c);
}
@SuppressWarnings("static-access")
@Override
public EventSet createEventSet(Document doc) {
EventSet es = new EventSet(doc.getAuthor());
char[] text = doc.getProcessedText();
String stringText = new String(text);
// use MaxentPOSTagsEventDriver's tagger
// initialize tagger and return empty event set if encountered a problem
if (tagger == null) {
tagger = MaxentPOSTagsEventDriver.initTagger();
if (tagger == null) return es;
}
List<List<HasWord>> sentences =
tagger.tokenizeText(new BufferedReader(new StringReader(stringText)));
ArrayList<TaggedWord> tagged = new ArrayList<TaggedWord>();
for (List<HasWord> sentence : sentences) tagged.addAll(tagger.tagSentence(sentence));
int i, j, n;
try {
n = Integer.parseInt(getParameter("N"));
} catch (NumberFormatException e) {
n = 2;
}
String curr;
for (i = 0; i < tagged.size() - n + 1; i++) {
curr = "(" + tagged.get(i).tag() + ")";
for (j = 1; j < n; j++) {
curr += "-(" + tagged.get(i + j).tag() + ")";
}
es.addEvent(new Event(curr));
}
sentences.clear();
sentences = null;
return es;
}
public static ArrayList<TaggedWord> Preprocess(ArrayList<TaggedWord> taggedWords) {
ArrayList<TaggedWord> newList = new ArrayList<TaggedWord>();
String[] punctuationsAndSpecialCharacters = {
",", ".", "?", "!", ":", ";", "\"", "-", "--", "'", "-LRB-", "-RRB-", "''", "``", "&"
}; // , "/", "\\", "<", ">", "#", "&", "*", "(", ")", "{", "}", "[", "]", "~", "|"};
HashMap<String, String> punctuationMarks = new HashMap<String, String>();
for (int i = 0; i < punctuationsAndSpecialCharacters.length; i++) {
punctuationMarks.put(
punctuationsAndSpecialCharacters[i], punctuationsAndSpecialCharacters[i]);
}
for (int i = 0; i < taggedWords.size(); i++) {
String word = taggedWords.get(i).word();
String posTag = taggedWords.get(i).tag();
if (!punctuationMarks.containsKey(word)) {
if (!(posTag.length() > 2 && posTag.substring(0, 3).equals("NNP"))) {
word = Morphology.lemmaStatic(word, posTag, true);
word = word.replace('-', ' ');
}
String newWord, newPosTag;
if (word.equals("n't")) newWord = "not";
else if (word.equals("'s")) newWord = "is";
else if (word.equals("'ll")) newWord = "will";
else if (word.equals("'m") || word.equals("m")) newWord = "am";
else if (word.equals("im")) newWord = "am";
else newWord = word;
newPosTag = posTag;
newList.add(new TaggedWord(newWord, newPosTag));
}
}
newList = StopWordRemoval(newList);
return newList;
}
public static double LexicalSimilarityScoreWordNet(
String sentence1, String sentence2, LeskWSD tm, LexicalizedParser lp, WordNetSimilarity ws) {
ArrayList<TaggedWord> taggedWordsPrev1 = Preprocess(StanfordParse(sentence1, lp));
ArrayList<TaggedWord> taggedWordsPrev2 = Preprocess(StanfordParse(sentence2, lp));
ArrayList<TaggedWord> taggedWords1 = new ArrayList<TaggedWord>();
ArrayList<TaggedWord> taggedWords2 = new ArrayList<TaggedWord>();
WordNetSense[] sensesPrev1 = tm.LeskJWI(sentence1);
WordNetSense[] sensesPrev2 = tm.LeskJWI(sentence2);
// System.out.println("Senses found!");
ArrayList<WordNetSense> senses1 = new ArrayList<WordNetSense>();
ArrayList<WordNetSense> senses2 = new ArrayList<WordNetSense>();
for (int i = 0; i < taggedWordsPrev1.size(); i++) {
String word = taggedWordsPrev1.get(i).word();
String posTag = taggedWordsPrev1.get(i).tag();
if (posTag.length() >= 2 && posTag.substring(0, 2).equals("NN")) {
taggedWords1.add(new TaggedWord(word, "NN"));
senses1.add(sensesPrev1[i]);
} else if (posTag.length() >= 2 && posTag.substring(0, 2).equals("VB")) {
taggedWords1.add(new TaggedWord(word, "VB"));
senses1.add(sensesPrev1[i]);
}
}
for (int i = 0; i < taggedWordsPrev2.size(); i++) {
String word = taggedWordsPrev2.get(i).word();
String posTag = taggedWordsPrev2.get(i).tag();
if (posTag.length() >= 2 && posTag.substring(0, 2).equals("NN")) {
taggedWords2.add(new TaggedWord(word, "NN"));
senses2.add(sensesPrev2[i]);
} else if (posTag.length() >= 2 && posTag.substring(0, 2).equals("VB")) {
taggedWords2.add(new TaggedWord(word, "VB"));
senses2.add(sensesPrev2[i]);
}
}
// System.out.println(taggedWords1.size() + "," + taggedWords2.size());
// array of edge weights with default weight 0
int length1 = taggedWords1.size();
int length2 = taggedWords2.size();
int arrSize = Math.max(length1, length2);
double[][] array = new double[arrSize][arrSize];
for (int i = 0; i < arrSize; i++) {
for (int j = 0; j < arrSize; j++) {
array[i][j] = 0;
}
}
for (int i = 0; i < length1; i++) {
for (int j = 0; j < length2; j++) {
String word1 = taggedWords1.get(i).word();
String posTag1 = taggedWords1.get(i).tag();
String word2 = taggedWords2.get(j).word();
String posTag2 = taggedWords2.get(j).tag();
double edgeWeight = 0;
// LSA Similarity
// edgeWeight = LSASimilarity.LSAWordSimilarity(word1, word2);
// DISCO Similarity
// DISCOSimilarity discoObj = new DISCOSimilarity();
try {
if (word1.compareToIgnoreCase(word2) == 0) edgeWeight = 1;
else {
// edgeWeight = ws.wuPalmerSimilarity(senses1.get(i), senses2.get(j));
edgeWeight = ws.linSimilarity(senses1.get(i), senses2.get(j));
}
} catch (Exception ex) {
ex.printStackTrace();
}
array[i][j] = edgeWeight;
}
}
// System.out.println("Hungarian starts " + arrSize);
double finalScore;
String sumType = "max";
int minLength = Math.min(length1, length2);
// finalScore = HungarianAlgorithm.hgAlgorithm(array, sumType)/minLength * 5;
if (arrSize == 0) finalScore = 0;
else finalScore = HungarianAlgorithm.hgAlgorithm(array, sumType) / arrSize * 5;
return finalScore;
}
public static double LexicalSimilarityScoreMax(
ArrayList<TaggedWord> taggedWords1,
ArrayList<TaggedWord> taggedWords2,
DISCOSimilarity discoRAM,
LexicalizedParser lp) {
// System.out.println(taggedWords1.size() + "," + taggedWords2.size());
// array of edge weights with default weight 0
int length1 = taggedWords1.size();
int length2 = taggedWords2.size();
int arrSize = Math.max(length1, length2);
double[][] array = new double[arrSize][arrSize];
for (int i = 0; i < arrSize; i++) {
for (int j = 0; j < arrSize; j++) {
array[i][j] = 0;
}
}
for (int i = 0; i < length1; i++) {
for (int j = 0; j < length2; j++) {
String word1 = taggedWords1.get(i).word();
String posTag1 = taggedWords1.get(i).tag();
String word2 = taggedWords2.get(j).word();
String posTag2 = taggedWords2.get(j).tag();
ArrayList<TaggedWord> newList1 = new ArrayList<TaggedWord>();
if (posTag1.length() >= 3 && posTag1.substring(0, 3).equals("NNP")) {
newList1.add(taggedWords1.get(i));
} else {
String[] words = word1.split(" ");
for (int k = 0; k < words.length; k++) newList1.add(new TaggedWord(words[k], posTag1));
}
ArrayList<TaggedWord> newList2 = new ArrayList<TaggedWord>();
if (posTag2.length() >= 3 && posTag2.substring(0, 3).equals("NNP")) {
newList2.add(taggedWords2.get(j));
} else {
String[] words = word2.split(" ");
for (int k = 0; k < words.length; k++) newList2.add(new TaggedWord(words[k], posTag2));
}
double edgeWeight = LexicalSimilarityScoreMin(newList1, newList2, discoRAM, lp);
array[i][j] = edgeWeight;
}
}
// System.out.println("Hungarian starts " + arrSize);
double finalScore;
String sumType = "max";
// int minLength = Math.min(length1, length2);
// finalScore = HungarianAlgorithm.hgAlgorithm(array, sumType)/minLength * 5;
finalScore = HungarianAlgorithm.hgAlgorithm(array, sumType) / arrSize * 5;
return finalScore;
}
/**
* Do max language model markov segmentation. Note that this algorithm inherently tags words as it
* goes, but that we throw away the tags in the final result so that the segmented words are
* untagged. (Note: for a couple of years till Aug 2007, a tagged result was returned, but this
* messed up the parser, because it could use no tagging but the given tagging, which often wasn't
* very good. Or in particular it was a subcategorized tagging which never worked with the current
* forceTags option which assumes that gold taggings are inherently basic taggings.)
*
* @param s A String to segment
* @return The list of segmented words.
*/
private ArrayList<HasWord> segmentWordsWithMarkov(String s) {
int length = s.length();
// Set<String> POSes = (Set<String>) POSDistribution.keySet(); // 1.5
int numTags = POSes.size();
// score of span with initial word of this tag
double[][][] scores = new double[length][length + 1][numTags];
// best (length of) first word for this span with this tag
int[][][] splitBacktrace = new int[length][length + 1][numTags];
// best tag for second word over this span, if first is this tag
int[][][] POSbacktrace = new int[length][length + 1][numTags];
for (int i = 0; i < length; i++) {
for (int j = 0; j < length + 1; j++) {
Arrays.fill(scores[i][j], Double.NEGATIVE_INFINITY);
}
}
// first fill in word probabilities
for (int diff = 1; diff <= 10; diff++) {
for (int start = 0; start + diff <= length; start++) {
int end = start + diff;
StringBuilder wordBuf = new StringBuilder();
for (int pos = start; pos < end; pos++) {
wordBuf.append(s.charAt(pos));
}
String word = wordBuf.toString();
for (String tag : POSes) {
IntTaggedWord itw = new IntTaggedWord(word, tag, wordIndex, tagIndex);
double score = lex.score(itw, 0, word, null);
if (start == 0) {
score += Math.log(initialPOSDist.probabilityOf(tag));
}
scores[start][end][itw.tag()] = score;
splitBacktrace[start][end][itw.tag()] = end;
}
}
}
// now fill in word combination probabilities
for (int diff = 2; diff <= length; diff++) {
for (int start = 0; start + diff <= length; start++) {
int end = start + diff;
for (int split = start + 1; split < end && split - start <= 10; split++) {
for (String tag : POSes) {
int tagNum = tagIndex.indexOf(tag, true);
if (splitBacktrace[start][split][tagNum] != split) {
continue;
}
Distribution<String> rTagDist = markovPOSDists.get(tag);
if (rTagDist == null) {
continue; // this happens with "*" POS
}
for (String rTag : POSes) {
int rTagNum = tagIndex.indexOf(rTag, true);
double newScore =
scores[start][split][tagNum]
+ scores[split][end][rTagNum]
+ Math.log(rTagDist.probabilityOf(rTag));
if (newScore > scores[start][end][tagNum]) {
scores[start][end][tagNum] = newScore;
splitBacktrace[start][end][tagNum] = split;
POSbacktrace[start][end][tagNum] = rTagNum;
}
}
}
}
}
}
int nextPOS = ArrayMath.argmax(scores[0][length]);
ArrayList<HasWord> words = new ArrayList<HasWord>();
int start = 0;
while (start < length) {
int split = splitBacktrace[start][length][nextPOS];
StringBuilder wordBuf = new StringBuilder();
for (int i = start; i < split; i++) {
wordBuf.append(s.charAt(i));
}
String word = wordBuf.toString();
// String tag = tagIndex.get(nextPOS);
// words.add(new TaggedWord(word, tag));
words.add(new Word(word));
if (split < length) {
nextPOS = POSbacktrace[start][length][nextPOS];
}
start = split;
}
return words;
}
