List of usage examples for edu.stanford.nlp.parser.nndep DependencyParser DEFAULT_MODEL
String DEFAULT_MODEL
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From source file:Dependency.java
public static void main(String[] args) { String modelPath = DependencyParser.DEFAULT_MODEL; String taggerPath = "edu/stanford/nlp/models/pos-tagger/english-left3words/english-left3words-distsim.tagger"; Scanner sc = new Scanner(System.in); String text = ""; text = sc.nextLine();/*from w ww . j a v a 2 s . c o m*/ // while(text!="exit"){ MaxentTagger tagger = new MaxentTagger(taggerPath); DependencyParser parser = DependencyParser.loadFromModelFile(modelPath); DocumentPreprocessor tokenizer = new DocumentPreprocessor(new StringReader(text)); for (List<HasWord> sentence : tokenizer) { List<TaggedWord> tagged = tagger.tagSentence(sentence); Object[] x = tagged.toArray(); GrammaticalStructure gs = parser.predict(tagged); //System.out.println(); Collection<TypedDependency> s = gs.typedDependenciesCollapsedTree(); Object[] z = s.toArray(); System.out.println(tagged.toString()); String token[] = new String[z.length]; String pos[] = new String[z.length]; int k = 0; for (Object i : x) { String str = i.toString(); /*String temp0="(.*?)(?=\\/)"; String temp1="\\/(.*)"; System.out.println(str); Pattern t0 = Pattern.compile("(.*?)(?=\\/)"); Pattern t1 = Pattern.compile("\\/(.*)"); Matcher m0 = t0.matcher(str); Matcher m1 = t1.matcher(str);*/ int index = str.lastIndexOf('/'); token[k] = str.substring(0, index); pos[k] = str.substring(index + 1); //System.out.println(pos[k]); k++; } String rels[] = new String[z.length]; String word1[] = new String[z.length]; String word2[] = new String[z.length]; int j = 0; for (Object i : z) { System.out.println(i); String temp = i.toString(); String pattern0 = "(.*)(?=\\()"; String pattern1 = "(?<=\\()(.*?)(?=-)"; String pattern2 = "(?<=, )(.*)(?=-)"; Pattern r0 = Pattern.compile(pattern0); Pattern r1 = Pattern.compile(pattern1); Pattern r2 = Pattern.compile(pattern2); Matcher m0 = r0.matcher(temp); Matcher m1 = r1.matcher(temp); Matcher m2 = r2.matcher(temp); if (m0.find()) { rels[j] = m0.group(0); //System.out.println(rels[j]); } if (m1.find()) { word1[j] = m1.group(0); } if (m2.find()) { word2[j] = m2.group(0); } j++; } //System.out.println(s); //Rules for feature extraction. //rule1::::::::::::::::: //System.out.println("1"); int[] q = toIntArray(grRecog(rels, "nsubj")); //System.out.println("2"); if (q.length != 0) { //System.out.println("3"); if (posrecog(token, pos, word2[q[0]]).equals("NN")) { //System.out.println("4"); int[] w = toIntArray(grRecog(rels, "compound")); //System.out.println("5"); if (w.length != 0) { System.out.println("6"); System.out.println(word1[q[0]] + "," + word2[q[0]] + "," + word2[w[0]]); } else { int conj_and_index = compgrRecog(rels, word1, word2, "conj:and", word2[q[0]]); if (conj_and_index != -1) { System.out.println( word1[conj_and_index] + "," + word2[conj_and_index] + "," + word2[q[0]]); } else System.out.println(word1[q[0]] + "," + word2[q[0]]); } } //RULE 2::::::::::::: else if (posrecog(token, pos, word1[q[0]]).equals("JJ")) { //System.out.println("aaaaa_JJ"); int a = compgrRecog(rels, word1, word2, "xcomp", word1[q[0]]); if (a != -1) { int b = compgrRecog(rels, word1, word2, "dobj", word2[a]); if (b != -1) { int c = compgrRecog(rels, word1, word2, "compound", word2[b]); if (c != -1) { System.out.println(word1[q[0]] + "," + word1[c] + "," + word2[c]); } } } //RULE 3:::::::::: else { int b[] = toIntArray(grRecog(rels, "ccomp")); if (b.length != 0) { System.out.println(word1[q[1]] + "," + word2[q[1]] + "," + word1[b[0]]); } } } //RULE 4:::::::::: else if (posrecog(token, pos, word1[q[0]]).equals("VBZ")) { //System.out.println("aaaaa"); int vbp_dobj_index = compgrRecog(rels, word1, word2, "dobj", word2[q[0]]); if (vbp_dobj_index != -1) { System.out.println(word1[vbp_dobj_index] + "," + word2[vbp_dobj_index]); } else { int vbp_xcomp_index = compgrRecog(rels, word1, word2, "xcomp", word1[q[0]]); if (vbp_xcomp_index != -1) { System.out.println(word1[vbp_xcomp_index] + "," + word2[vbp_xcomp_index]); } else { int vbp_acomp_index = compgrRecog(rels, word1, word2, "acomp", word1[q[0]]); if (vbp_acomp_index != -1) { System.out.println( word1[q[0]] + "," + word1[vbp_acomp_index] + "," + word2[vbp_acomp_index]); } else System.out.println(word1[q[0]]); } } } int[] f = toIntArray(grRecog(rels, "amod")); if (f.length != 0) { for (int i : f) { System.out.println(word1[i] + "," + word2[i]); } int cj[] = toIntArray(grRecog(rels, "conj:and")); if (cj.length != 0) { for (int i : cj) { System.out.println(word1[i] + "," + word2[i]); } } } int[] neg = toIntArray(grRecog(rels, "neg")); if (neg.length != 0) { for (int i : neg) { System.out.println(word1[i] + "," + word2[i]); } } } else { int[] f = toIntArray(grRecog(rels, "amod")); if (f.length != 0) { for (int i : f) { System.out.print(word1[i] + "," + word2[i]); String qwe = word1[i] + "," + word2[i]; } int cj[] = toIntArray(grRecog(rels, "conj:and")); if (cj.length != 0) { for (int i : cj) { System.out.println(word2[i]); } } } int[] neg = toIntArray(grRecog(rels, "neg")); if (neg.length != 0) { for (int i : neg) { System.out.println(word1[i] + "," + word2[i]); } } } //RULE 2::::::::::::: } // text=sc.nextLine(); //} }
From source file:DependencyParserDemo.java
public static void main(String[] args) { String modelPath = DependencyParser.DEFAULT_MODEL; String taggerPath = "edu/stanford/nlp/models/pos-tagger/english-left3words/english-left3words-distsim.tagger"; for (int argIndex = 0; argIndex < args.length;) { switch (args[argIndex]) { case "-tagger": taggerPath = args[argIndex + 1]; argIndex += 2;/* ww w. j a v a 2 s . c o m*/ break; case "-model": modelPath = args[argIndex + 1]; argIndex += 2; break; default: throw new RuntimeException("Unknown argument " + args[argIndex]); } } String text = "I can almost always tell when movies use fake dinosaurs."; MaxentTagger tagger = new MaxentTagger(taggerPath); DependencyParser parser = DependencyParser.loadFromModelFile(modelPath); DocumentPreprocessor tokenizer = new DocumentPreprocessor(new StringReader(text)); for (List<HasWord> sentence : tokenizer) { List<TaggedWord> tagged = tagger.tagSentence(sentence); GrammaticalStructure gs = parser.predict(tagged); // Print typed dependencies System.err.println(gs); } }
From source file:Dependency2.java
public static void main(String[] args) { String modelPath = DependencyParser.DEFAULT_MODEL; String taggerPath = "edu/stanford/nlp/models/pos-tagger/english-left3words/english-left3words-distsim.tagger"; Scanner sc = new Scanner(System.in); readCsv();/* w ww .java 2 s .c o m*/ String text = ""; text = sc.nextLine(); if (multifeatures(text)) { System.out.println("Multiple features present"); MaxentTagger tagger = new MaxentTagger(taggerPath); DependencyParser parser = DependencyParser.loadFromModelFile(modelPath); DocumentPreprocessor tokenizer = new DocumentPreprocessor(new StringReader(text)); for (List<HasWord> sentence : tokenizer) { List<TaggedWord> tagged = tagger.tagSentence(sentence); GrammaticalStructure gs = parser.predict(tagged); Collection<TypedDependency> s = gs.typedDependenciesCollapsedTree(); Map<Character, Pair<Character, Character>> map = new HashMap<Character, Pair<Character, Character>>(); Object[] z = s.toArray(); String rels[] = new String[z.length]; String word1[] = new String[z.length]; String word2[] = new String[z.length]; int j = 0; String f, f1, f2; for (Object i : z) { //System.out.println(i); String temp = i.toString(); System.out.println(temp); String pattern0 = "(.*)(?=\\()"; String pattern1 = "(?<=\\()(.*?)(?=-)"; String pattern2 = "(?<=,)(.*)(?=-)"; Pattern r0 = Pattern.compile(pattern0); Pattern r1 = Pattern.compile(pattern1); Pattern r2 = Pattern.compile(pattern2); Matcher m0 = r0.matcher(temp); Matcher m1 = r1.matcher(temp); Matcher m2 = r2.matcher(temp); if (m0.find()) rels[j] = m0.group(0); if (m1.find()) word1[j] = m1.group(0); if (m2.find()) word2[j] = m2.group(0); if (rels[j].equals("amod")) { f1 = getFeature(word1[j]); f2 = getFeature(word2[j]); f = f1 != null ? (f1) : (f2 != null ? f2 : null); if (f != null) { System.out.println("Feature: " + f); } } j++; } //System.out.println(Arrays.toString(rels)); } } else { //sentence score is feature score } }
From source file:RestServices.GetTargetedSentimentResource.java
/** * Retrieves representation of an instance of RestServices.GetTargetedSentimentResource * @return an instance of java.lang.String *///from w w w. j a va2 s . co m @GET @Produces("application/json") public String getJson(@QueryParam("data") String datas) { System.out.println("Working Directory = " + System.getProperty("user.dir")); JSONObject objOuter = new JSONObject(); try { JSONObject inputJsonObject = new JSONObject(datas); String targetPhrase = inputJsonObject.getString("target"); String contextText = inputJsonObject.getString("data"); String modelPath = DependencyParser.DEFAULT_MODEL; MaxentTagger tagger = new MaxentTagger(GlobalVarsStore.taggerPath); DependencyParser parser = DependencyParser.loadFromModelFile(modelPath); DocumentPreprocessor tokenizer = new DocumentPreprocessor(new StringReader(contextText)); ArrayList<TypedDependency> td = new ArrayList<TypedDependency>(); for (List<HasWord> sentence : tokenizer) { List<TaggedWord> tagged = tagger.tagSentence(sentence); GrammaticalStructure gs = parser.predict(tagged); td.addAll(gs.typedDependencies()); } BaselineAnalysisTools bat = new BaselineAnalysisTools(); bat.prepareTools(); String[] bag = clear(contextText).split("(?:(?:[^a-zA-Z]+')|(?:'[^a-zA-Z]+))|(?:[^a-zA-Z']+)"); double sent = 0.0; ArrayList<Double> weights = new ArrayList<Double>(); int pos = 0; int neg = 0; int neu = 0; double confidenceContainment = 0.0; double confidenceClassification = 0.0; for (int i = 0; i < bag.length; i++) { Double res = GlobalVarsStore.lex.testWord(bag[i]); if (res != null) { weights.add(res); confidenceContainment++; } } Double totalSent = 0.0; for (int i = 0; i < weights.size(); i++) { totalSent += weights.get(i); if (weights.get(i) > 0) { pos++; } else if (weights.get(i) < 0) { neg++; } else { neu++; } } if (weights.size() > 0) { sent = totalSent / weights.size(); confidenceContainment = confidenceContainment / bag.length; if (sent > 0) { confidenceClassification = pos / (double) (pos + neg + neu); } else if (sent < 0) { confidenceClassification = neg / (double) (pos + neg + neu); } else { confidenceClassification = neu / (double) (pos + neg + neu); } } String[] targets; if (targetPhrase.contains(" ")) { targets = targetPhrase.split(" "); } else { targets = new String[1]; targets[0] = targetPhrase; } double tempSent = 0; double tSent = 0; double tSize = 0; double sentMod = 1; for (int i = 0; i < targets.length; i++) { for (int j = 0; j < td.size(); j++) { String secondLevel = null; double secondSentMod = 1; if (targets[i].equals(td.get(j).gov().value())) { if (td.get(j).reln().getShortName().equals("neg")) { sentMod = -1; } else { if (td.get(j).reln().getShortName().equals("dobj")) { secondLevel = td.get(j).dep().value(); } tSize++; tempSent = GlobalVarsStore.lex.testWord(td.get(j).dep().value()); } } else if (targets[i].equals(td.get(j).dep().value())) { if (td.get(j).reln().getShortName().equals("neg")) { sentMod = -1; } else { if (td.get(j).reln().getShortName().equals("dobj")) { secondLevel = td.get(j).gov().value(); } tSize++; tempSent = GlobalVarsStore.lex.testWord(td.get(j).gov().value()); } } if (secondLevel != null) { for (int k = 0; k < td.size(); k++) { if (!targets[i].equals(td.get(k).dep().value()) && secondLevel.equals(td.get(k).gov().value())) { if (td.get(k).reln().getShortName().equals("neg")) { secondSentMod = -1; } } else if (!targets[i].equals(td.get(k).gov().value()) && secondLevel.equals(td.get(k).dep().value())) { if (td.get(k).reln().getShortName().equals("neg")) { secondSentMod = -1; } } } } tSent += tempSent * secondSentMod; } } if (tSize > 0) { tSent /= tSize; } if (tSent == 0) { tSent = sent * sentMod; } objOuter.put("SOS", tSent); objOuter.put("CONFIDENCE", (confidenceClassification * (1 - GlobalVarsStore.containmentConfidenceWeight)) + (confidenceContainment * GlobalVarsStore.containmentConfidenceWeight)); } catch (JSONException ex) { Logger.getLogger(GetBatchSentimentResource.class.getName()).log(Level.SEVERE, null, ex); } return objOuter.toString(); }
From source file:RestServices.Scoresservice.java
public static void main(String[] args) throws ParseException { /* String scenario="transportation"; GlobalVarsStore.lexicon="wordnet";// w w w. ja va2s.c om int objective=6; //Reading source file CrawlersConnector ccn=new CrawlersConnector(); ArrayList<CustomStatus> scenarioTweets = null; try { scenarioTweets = ccn.readScenario(scenario); } catch (IOException ex) { System.out.println("<xml><result>Error: "+ex.getMessage()+"</result></xml>"); } catch (JSONException ex) { System.out.println("<xml><result>Error: "+ex.getMessage()+"</result></xml>"); } System.out.println("Parsed " + scenarioTweets.size() + " documents."); ArrayList<String> objectiveNames=new ArrayList<String>(); objectiveNames.add("Change in Level of Service"); objectiveNames.add("% change of Accidents cost"); objectiveNames.add("% change of Air pollution (external) cost"); objectiveNames.add("% change of Noise (external) cost"); objectiveNames.add("User convenience in using the RP system"); objectiveNames.add("Availability of alternative routes and modes"); //Calculating SOF DecimalFormat df = new DecimalFormat("#.####"); ArrayList<String> keys=null; if(scenario.equalsIgnoreCase("transportation")){ if(objective==1){keys=ccn.transportKeywords1;} else if(objective==2){keys=ccn.transportKeywords2;} else if(objective==3){keys=ccn.transportKeywords3;} else if(objective==4){keys=ccn.transportKeywords4;} else if(objective==5){keys=ccn.transportKeywords5;} else if(objective==6){keys=ccn.transportKeywords6;} }else if(scenario.equalsIgnoreCase("biofuel")){ if(objective==1){keys=ccn.biofuelKeywords1;} else if(objective==2){keys=ccn.biofuelKeywords2;} else if(objective==3){keys=ccn.biofuelKeywords3;} else if(objective==4){keys=ccn.biofuelKeywords4;} } System.out.println("Calculating Score Of Frequency..."); TopicAnalysisTools tat = new TopicAnalysisTools(); ArrayList<Double> sofs = new ArrayList<Double>(); for (int i = 0; i < keys.size(); i++) { sofs.add(tat.countFrequency(scenarioTweets, keys.get(i))); } Double sof=0.0; for (int i = 0; i < sofs.size(); i++) { sof+=sofs.get(i); } if(sofs.size()>0) sof=sof/sofs.size(); System.out.println("Score of Frequency for objective '" + objectiveNames.get(objective-1) + "' is " + df.format(sof)); //Calculating SOS System.out.println("Calculating Score Of Sentiment..."); BaselineAnalysisTools bat = new BaselineAnalysisTools(); bat.prepareTools(); ArrayList<CustomStatus> tweets=null; Double sos=0.0; ArrayList<Double> soses = new ArrayList<Double>(); for (int i = 0; i < keys.size(); i++) { try { tweets=ccn.readKeyword(keys.get(i)); soses.add(bat.SentiWordNetMeanAnalysis(tweets,keys.get(i))); } catch (IOException ex) { ex.printStackTrace(); soses.add(0.0); } catch (JSONException ex) { ex.printStackTrace();soses.add(0.0); } } for (int i = 0; i < soses.size(); i++) { sos+=soses.get(i); } if(soses.size()>0) sos=sos/soses.size(); System.out.println("Score of Sentiment for objective '" + objectiveNames.get(objective-1) + "' is " + df.format(sos));*/ //String tweetDate="Thu Jul 23 00:00:00 CEST 2015"; //DateFormat df = new SimpleDateFormat("EEE MMM dd kk:mm:ss z yyyy", Locale.ENGLISH); //Date result = df.parse(tweetDate); //tweetDate = (result.getYear()+1900)+"-"+(result.getMonth()+1)+"-"+result.getDate(); //System.out.println(tweetDate); String modelPath = DependencyParser.DEFAULT_MODEL; String taggerPath = "C:\\Users\\ViP\\Copy\\NTUA\\Code\\ConsensusPublicOpinion\\models\\english-left3words-distsim.tagger"; for (int argIndex = 0; argIndex < args.length;) { switch (args[argIndex]) { case "-tagger": taggerPath = args[argIndex + 1]; argIndex += 2; break; case "-model": modelPath = args[argIndex + 1]; argIndex += 2; break; default: throw new RuntimeException("Unknown argument " + args[argIndex]); } } String text = "I love apples and do not hate oranges"; MaxentTagger tagger = new MaxentTagger(taggerPath); DependencyParser parser = DependencyParser.loadFromModelFile(modelPath); DocumentPreprocessor tokenizer = new DocumentPreprocessor(new StringReader(text)); for (List<HasWord> sentence : tokenizer) { List<TaggedWord> tagged = tagger.tagSentence(sentence); GrammaticalStructure gs = parser.predict(tagged); // Print typed dependencies System.out.println(gs); ArrayList<TypedDependency> cd = (ArrayList<TypedDependency>) gs.typedDependencies(); for (int i = 0; i < cd.size(); i++) { System.out.println(String.format("%1$" + 10 + "s", cd.get(i).gov().value()) + "\t" + String.format("%1$" + 10 + "s", cd.get(i).dep().value()) + "\t" + cd.get(i).reln().getShortName() + "\t" + cd.get(i).reln().getLongName()); } } }