List of usage examples for edu.stanford.nlp.trees Trees convertToCoreLabels
public static void convertToCoreLabels(Tree tree)
From source file:BuildBinarizedDataset.java
/** * Turns a text file into trees for use in a RNTN classifier such as * the treebank used in the Sentiment project. * <br>/*from www.j a v a 2s . com*/ * The expected input file is one sentence per line, with sentences * separated by blank lines. The first line has the main label of the sentence together with the full sentence. * Lines after the first sentence line but before * the blank line will be treated as labeled sub-phrases. The * labels should start with the label and then contain a list of * tokens the label applies to. All phrases that do not have their own label will take on the main sentence label! * For example: * <br> * <code> * 1 Today is not a good day.<br> * 3 good<br> * 3 good day <br> * 3 a good day <br> * <br> * (next block starts here) <br> * </code> * By default the englishPCFG parser is used. This can be changed * with the <code>-parserModel</code> flag. Specify an input file * with <code>-input</code>. * <br> * If a sentiment model is provided with -sentimentModel, that model * will be used to prelabel the sentences. Any spans with given * labels will then be used to adjust those labels. */ public static void main(String[] arg) throws IOException { CollapseUnaryTransformer transformer = new CollapseUnaryTransformer(); // FileWriter writer = new FileWriter("D:\\dataset\\train.txt", true); String parserModel = "edu/stanford/nlp/models/lexparser/englishPCFG.ser.gz"; String args[] = { "-input", "D:\\parse.txt", "-sentimentModel", "edu/stanford/nlp/models/sentiment/sentiment.ser.gz" }; String inputPath = "D:\\dataset\\good.txt"; String sentimentModelPath = "edu/stanford/nlp/models/sentiment/sentiment.ser.gz"; SentimentModel sentimentModel = null; /* for (int argIndex = 0; argIndex < args.length; ) { if (args[argIndex].equalsIgnoreCase("-input")) { inputPath = args[argIndex + 1]; argIndex += 2; } else if (args[argIndex].equalsIgnoreCase("-parserModel")) { parserModel = args[argIndex + 1]; argIndex += 2; } else if (args[argIndex].equalsIgnoreCase("-sentimentModel")) { sentimentModelPath = args[argIndex + 1]; argIndex += 2; } else { System.err.println("Unknown argument " + args[argIndex]); System.exit(2); } }*/ if (inputPath == null) { throw new IllegalArgumentException("Must specify input file with -input"); } LexicalizedParser parser = LexicalizedParser.loadModel(parserModel); TreeBinarizer binarizer = TreeBinarizer.simpleTreeBinarizer(parser.getTLPParams().headFinder(), parser.treebankLanguagePack()); if (sentimentModelPath != null) { sentimentModel = SentimentModel.loadSerialized(sentimentModelPath); } String text = IOUtils.slurpFileNoExceptions(inputPath); String[] chunks = text.split("\\n\\s*\\n+"); // need blank line to make a new chunk for (String chunk : chunks) { if (chunk.trim().isEmpty()) { continue; } // The expected format is that line 0 will be the text of the // sentence, and each subsequence line, if any, will be a value // followed by the sequence of tokens that get that value. // Here we take the first line and tokenize it as one sentence. String[] lines = chunk.trim().split("\\n"); String sentence = lines[0]; StringReader sin = new StringReader(sentence); DocumentPreprocessor document = new DocumentPreprocessor(sin); document.setSentenceFinalPuncWords(new String[] { "\n" }); List<HasWord> tokens = document.iterator().next(); Integer mainLabel = new Integer(tokens.get(0).word()); //System.out.print("Main Sentence Label: " + mainLabel.toString() + "; "); tokens = tokens.subList(1, tokens.size()); //System.err.println(tokens); Map<Pair<Integer, Integer>, String> spanToLabels = Generics.newHashMap(); for (int i = 1; i < lines.length; ++i) { extractLabels(spanToLabels, tokens, lines[i]); } // TODO: add an option which treats the spans as constraints when parsing Tree tree = parser.apply(tokens); Tree binarized = binarizer.transformTree(tree); Tree collapsedUnary = transformer.transformTree(binarized); // if there is a sentiment model for use in prelabeling, we // label here and then use the user given labels to adjust if (sentimentModel != null) { Trees.convertToCoreLabels(collapsedUnary); SentimentCostAndGradient scorer = new SentimentCostAndGradient(sentimentModel, null); scorer.forwardPropagateTree(collapsedUnary); setPredictedLabels(collapsedUnary); } else { setUnknownLabels(collapsedUnary, mainLabel); //collapsedUnary.label().setValue(mainLabel.toString()); //System.out.println("Root"+collapsedUnary.getNodeNumber(1)); } Trees.convertToCoreLabels(collapsedUnary); collapsedUnary.indexSpans(); for (Map.Entry<Pair<Integer, Integer>, String> pairStringEntry : spanToLabels.entrySet()) { setSpanLabel(collapsedUnary, pairStringEntry.getKey(), pairStringEntry.getValue()); } String x = collapsedUnary.toString(); //x.replaceAll("\\s",""); x = x.replace("(", "["); x = x.replace(")", "]"); //writer.write(x); //writer.write("\r\n"); System.out.println(x); //System.out.println(); } //writer.close(); }
From source file:ConstituencyParse.java
License:Apache License
public int[] constTreeParents(Tree tree) { Tree binarized = binarizer.transformTree(tree); Tree collapsedUnary = transformer.transformTree(binarized); Trees.convertToCoreLabels(collapsedUnary); collapsedUnary.indexSpans();/*from w w w . j av a 2s. c o m*/ List<Tree> leaves = collapsedUnary.getLeaves(); int size = collapsedUnary.size() - leaves.size(); int[] parents = new int[size]; HashMap<Integer, Integer> index = new HashMap<Integer, Integer>(); int idx = leaves.size(); int leafIdx = 0; for (Tree leaf : leaves) { Tree cur = leaf.parent(collapsedUnary); // go to preterminal int curIdx = leafIdx++; boolean done = false; while (!done) { Tree parent = cur.parent(collapsedUnary); if (parent == null) { parents[curIdx] = 0; break; } int parentIdx; int parentNumber = parent.nodeNumber(collapsedUnary); if (!index.containsKey(parentNumber)) { parentIdx = idx++; index.put(parentNumber, parentIdx); } else { parentIdx = index.get(parentNumber); done = true; } parents[curIdx] = parentIdx + 1; cur = parent; curIdx = parentIdx; } } return parents; }
From source file:de.tudarmstadt.ukp.dkpro.core.stanfordnlp.StanfordDependencyConverter.java
License:Open Source License
@Override public void process(JCas aJCas) throws AnalysisEngineProcessException { String lang = language != null ? language : aJCas.getDocumentLanguage(); if (!languagePacks.containsKey(lang)) { throw new AnalysisEngineProcessException( new IllegalStateException("Unsupported language [" + aJCas.getDocumentLanguage() + "]")); }//from w w w . j a va 2 s .co m TreebankLanguagePack lp; try { lp = languagePacks.get(aJCas.getDocumentLanguage()).newInstance(); } catch (InstantiationException | IllegalAccessException e) { throw new AnalysisEngineProcessException(e); } List<CoreMap> sentences = new ArrayList<CoreMap>(); for (ROOT root : select(aJCas, ROOT.class)) { // Copy all relevant information from the tokens List<Token> tokens = selectCovered(Token.class, root); List<CoreLabel> coreTokens = new ArrayList<CoreLabel>(); for (Token token : tokens) { coreTokens.add(tokenToWord(token)); } // SemanticHeadFinder (nonTerminalInfo) does not know about PRN0, so we have to replace // it with PRN to avoid NPEs. TreeFactory tFact = new LabeledScoredTreeFactory(CoreLabel.factory()) { @Override public Tree newTreeNode(String aParent, List<Tree> aChildren) { String parent = aParent; if ("PRN0".equals(parent)) { parent = "PRN"; } Tree node = super.newTreeNode(parent, aChildren); return node; } }; Tree tree = TreeUtils.createStanfordTree(root, tFact); Trees.convertToCoreLabels(tree); tree.indexSpans(); // Build the sentence CoreMap sentence = new CoreLabel(); sentence.set(TreeAnnotation.class, tree); sentence.set(TokensAnnotation.class, coreTokens); sentence.set(RootKey.class, root); sentences.add(sentence); doCreateDependencyTags(aJCas, lp, tree, tokens); } }