List of usage examples for edu.stanford.nlp.parser.lexparser LexicalizedParser treebankLanguagePack
@Override
public TreebankLanguagePack treebankLanguagePack()
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>/* w w w . ja v a 2s . c o m*/ * 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:percobaan.ParserDemo.java
/** * demoAPI demonstrates other ways of calling the parser with * already tokenized text, or in some cases, raw text that needs to * be tokenized as a single sentence. Output is handled with a * TreePrint object. Note that the options used when creating the * TreePrint can determine what results to print out. Once again, * one can capture the output by passing a PrintWriter to * TreePrint.printTree. This code is for English. */// w ww. j a v a 2 s . com // public static void demoAPI(LexicalizedParser lp) { public void demoAPI(LexicalizedParser lp) { // This option shows parsing a list of correctly tokenized words String[] sent = { "This", "is", "an", "easy", "sentence", "." }; List<CoreLabel> rawWords = Sentence.toCoreLabelList(sent); Tree parse = lp.apply(rawWords); // parse.pennPrint(); // System.out.println(); // This option shows loading and using an explicit tokenizer // String sent2 = "Works great. Instructions were straightforward and easy to follow. Buying a second one just in case I need it in the future."; // for (int i = 0; i < data.lemmaKumpulans.size(); i++){ for (int i = 0; i < data.lemmaKumpulans.size(); i++) { String sent2 = data.lemmaKumpulans.get(i); TokenizerFactory<CoreLabel> tokenizerFactory = PTBTokenizer.factory(new CoreLabelTokenFactory(), ""); Tokenizer<CoreLabel> tok = tokenizerFactory.getTokenizer(new StringReader(sent2)); List<CoreLabel> rawWords2 = tok.tokenize(); parse = lp.apply(rawWords2); TreebankLanguagePack tlp = lp.treebankLanguagePack(); // PennTreebankLanguagePack for English GrammaticalStructureFactory gsf = tlp.grammaticalStructureFactory(); GrammaticalStructure gs = gsf.newGrammaticalStructure(parse); List<TypedDependency> tdl = gs.typedDependenciesCCprocessed(); // System.out.println(tdl); // System.out.println(); // You can also use a TreePrint object to print trees and dependencies TreePrint tp = new TreePrint("typedDependenciesCollapsed"); System.out.println(i + 1); tp.printTree(parse); //mengoutputkan hasil parsing } }