List of usage examples for edu.stanford.nlp.ling CoreLabel get
@Override @SuppressWarnings("unchecked") public <VALUE> VALUE get(Class<? extends Key<VALUE>> key)
From source file:DateRecognitionFunction.java
License:Apache License
public static Set<String> ner(String text) { Annotation document = new Annotation(text); // run all Annotators on this text pipeline.annotate(document);/*www .j av a 2 s . co m*/ // these are all the sentences in this document // a CoreMap is essentially a Map that uses class objects as keys and has values with custom types List<CoreMap> sentences = document.get(CoreAnnotations.SentencesAnnotation.class); Set<String> entities = new HashSet<>(); for (CoreMap sentence : sentences) { // traversing the words in the current sentence // a CoreLabel is a CoreMap with additional token-specific methods for (CoreLabel token : sentence.get(CoreAnnotations.TokensAnnotation.class)) { // this is the text of the token String word = token.get(CoreAnnotations.TextAnnotation.class); // this is the NER label of the token String ne = token.get(CoreAnnotations.NamedEntityTagAnnotation.class); if (ne.equals("DATE")) { entities.add(word); } } } return entities; }
From source file:PersonRecognitionFunction.java
License:Apache License
public static Set<String> ner(String text) { Annotation document = new Annotation(text); // run all Annotators on this text pipeline.annotate(document);//from w ww . ja va 2 s.co m // these are all the sentences in this document // a CoreMap is essentially a Map that uses class objects as keys and has values with custom types List<CoreMap> sentences = document.get(CoreAnnotations.SentencesAnnotation.class); Set<String> locations = new HashSet<>(); for (CoreMap sentence : sentences) { // traversing the words in the current sentence // a CoreLabel is a CoreMap with additional token-specific methods for (CoreLabel token : sentence.get(CoreAnnotations.TokensAnnotation.class)) { // this is the text of the token String word = token.get(CoreAnnotations.TextAnnotation.class); // this is the NER label of the token String ne = token.get(CoreAnnotations.NamedEntityTagAnnotation.class); if (ne.equals("PERSON")) { locations.add(word); } } } return locations; }
From source file:NERServer.java
License:Open Source License
public static void main(String[] args) throws Exception { if (args.length != 1) { System.err.println("usage: java NERServer modelpath"); System.exit(1);/*from w w w .j a v a 2 s . c om*/ } CRFClassifier crf = CRFClassifier.getClassifier(args[0]); BufferedReader input = new BufferedReader(new InputStreamReader(System.in), 1); for (;;) { String ln = input.readLine(); if (ln == null) { break; } List<List<CoreLabel>> out = crf.classify(ln); for (List<CoreLabel> sentence : out) { for (CoreLabel word : sentence) { String label = word.get(CoreAnnotations.AnswerAnnotation.class); System.out.print(word.word() + '/' + label + ' '); } } System.out.print('\n'); } }
From source file:Treeparse.java
public static void main(String[] args) { // TODO code application logic here Properties props = new Properties(); props.setProperty("annotators", "tokenize, ssplit, pos, lemma,parse"); StanfordCoreNLP pipeline = new StanfordCoreNLP(props); System.out.println("Enter the text:"); Scanner sc = new Scanner(System.in); text = sc.nextLine();/* w w w. j a v a 2s . c o m*/ //while(text!="exit") //{ Annotation document = new Annotation(text); pipeline.annotate(document); List<CoreMap> sentences = document.get(SentencesAnnotation.class); for (CoreMap sentence : sentences) { token_length = sentence.get(TokensAnnotation.class).size(); arr1 = new String[POSTagger.token_length]; arr2 = new String[POSTagger.token_length]; int i = 0, j = 0; // System.out.println("Size"+token_length); for (CoreLabel token : sentence.get(TokensAnnotation.class)) { String word = token.get(TextAnnotation.class); String pos = token.get(PartOfSpeechAnnotation.class); // String ner = token.get(NamedEntityTagAnnotation.class); } Tree tree = sentence.get(TreeAnnotation.class); // System.out.println(tree); List<Tree> x = GetNounPhrases(tree); System.out.println(x); // Print words and Pos Tags /*for (Tree leaf : leaves) { Tree parent = leaf.parent(tree); System.out.print(leaf.label().value() + "-" + parent.label().value() + " "); }*/ } //System.out.println("Enter the text:"); //text=sc.nextLine(); }
From source file:unCompressedIndex.java
public static String lemmatize(String documentText) { List<String> lemmas = new LinkedList<String>(); // Create an empty Annotation just with the given text Annotation document = new Annotation(documentText); // run all Annotators on this text pipeline.annotate(document);//from w w w . j a va2 s.c o m // Iterate over all of the sentences found String temp = ""; List<CoreMap> sentences = document.get(SentencesAnnotation.class); for (CoreMap sentence : sentences) { // Iterate over all tokens in a sentence for (CoreLabel token : sentence.get(TokensAnnotation.class)) { temp = token.get(LemmaAnnotation.class); lemmas.add(temp); } } return lemmas.get(0).toString(); //System.out.println("lemmas:"+lemmas.get(0)); }
From source file:BuildBinarizedDataset.java
public static boolean setSpanLabel(Tree tree, Pair<Integer, Integer> span, String value) { if (!(tree.label() instanceof CoreLabel)) { throw new AssertionError("Expected CoreLabels"); }//from ww w. j ava 2 s .co m CoreLabel label = (CoreLabel) tree.label(); if (label.get(CoreAnnotations.BeginIndexAnnotation.class).equals(span.first) && label.get(CoreAnnotations.EndIndexAnnotation.class).equals(span.second)) { label.setValue(value); return true; } if (label.get(CoreAnnotations.BeginIndexAnnotation.class) > span.first && label.get(CoreAnnotations.EndIndexAnnotation.class) < span.second) { return false; } for (Tree child : tree.children()) { if (setSpanLabel(child, span, value)) { return true; } } return false; }
From source file:LocationRecognitionFunction.java
License:Apache License
public static Set<String> ner(String text) { Annotation document = new Annotation(text); // run all Annotators on this text pipeline.annotate(document);/*from ww w . ja v a 2s. com*/ // these are all the sentences in this document // a CoreMap is essentially a Map that uses class objects as keys and has values with custom types List<CoreMap> sentences = document.get(CoreAnnotations.SentencesAnnotation.class); Set<String> locations = new HashSet<>(); for (CoreMap sentence : sentences) { // traversing the words in the current sentence // a CoreLabel is a CoreMap with additional token-specific methods for (CoreLabel token : sentence.get(CoreAnnotations.TokensAnnotation.class)) { // this is the text of the token String word = token.get(CoreAnnotations.TextAnnotation.class); // this is the NER label of the token String ne = token.get(CoreAnnotations.NamedEntityTagAnnotation.class); if (ne.equals("LOCATION")) { locations.add(word); } } } return locations; }
From source file:agk.chatbot.nlp.StanfordAnnotator.java
License:Open Source License
@Override public NLPText processString(String content) { NLPText txt = new NLPText(content); Annotation doc = new Annotation(content); pipeline.annotate(doc);//from w ww . j a v a 2 s. c o m for (CoreMap sentence : doc.get(SentencesAnnotation.class)) { String text = sentence.get(TextAnnotation.class); NLPSentence s = new NLPSentence(text); for (CoreLabel token : sentence.get(TokensAnnotation.class)) { String word = token.get(TextAnnotation.class); String pos = token.get(PartOfSpeechAnnotation.class); String lemma = token.get(LemmaAnnotation.class); String ner = token.get(NamedEntityTagAnnotation.class); NLPToken t = new NLPToken(word); t.setNerTag(ner); t.setPostag(pos); t.setStem(lemma); t.setMainName(!ner.equals("O")); t.setStopWord(NLPText.isStopWord(word)); s.addToken(t); } txt.addSentence(s); } return txt; }
From source file:analytics.weka.EnglishTextAnnotations.java
License:Apache License
public static void main(String[] args) { // creates a StanfordCoreNLP object, with POS tagging, lemmatization, // NER, parsing, and coreference resolution Properties props = new Properties(); props.setProperty("annotators", "tokenize, ssplit, pos, lemma, ner");//, parse, sentiment StanfordCoreNLP pipeline = new StanfordCoreNLP(props); // read some text in the text variable String text = "jackie brown ( miramax - 1997 ) starring pam grier , samuel l . jackson , robert forster , bridget fonda , michael keaton , robert de niro , michael bowen , chris tucker screenplay by quentin tarantino , based on the novel rum punch by elmore leonard produced by lawrence bender directed by quentin tarantino running time : 155 minutes note : some may consider portions of the following text to be spoilers . be forewarned . ------------------------------------------------------------- during the three years since the release of the groundbreaking success pulp fiction , the cinematic output from its creator , quentin tarantino , has been surprisingly low . oh , he\'s been busy -- doing the talk show circuit , taking small roles in various films , overseeing the production of his screenplay from dusk till dawn , making cameo appearances on television shows , providing a vignette for the ill-fated anthology four rooms -- everything , it seems , except direct another feature-length film . it\'s been the long intermission between projects as well as the dizzying peak which pulp fiction reached which has made mr . tarantino\'s new feature film , jackie brown , one of the most anticipated films of the year , and his third feature film cements his reputation as the single most important new american filmmaker to emerge from the 1990s . things aren\'t going well for jackie brown ( pam grier ) . she\'s 44 years old , stuck at a dead-end job ( \" $16 , 000 a year , plus retirement benefits that aren\'t worth a damn \" ) as a flight attendant for the worst airline in north america -- and she\'s just been caught at the airport by atf agent ray nicolette ( portrayed with terrific childlike enthusiasm by michael keaton ) and police officer mark dargus ( michael bowen ) smuggling $50 000 from mexico for gun-runner ordell robbie ( samuel l . jackson ) , who has her bailed out by unassuming bail bondsman max cherry ( robert forster ) . the loquacious ordell , based out of a hermosa beach house where his horny , bong-hitting surfer girl melanie ( bridget fonda ) and agreeable crony louis gara ( robert de niro ) hang out , operates under the policy that the best rat is a dead rat , and he\'s soon out to silence jackie brown . meanwhile , the authorities\' target is ordell , and they want jackie to help them by arranging a sting to the tune of a half-million dollars . only through a series of clever twists , turns , and double-crosses will jackie be able to gain the upper hand on both of her nemeses . although jackie brown marks mr . tarantino\'s first produced screenplay adaptation ( based on the elmore leonard novel \" rum punch \" ) , there\'s no mistaking his distinctive fingerprints all over this film . while he\'s adhered closely to the source material in a narrative sense , the setting has been relocated to los angeles and the lead character\'s now black . in terms of ambiance , the film harkens back to the 1970s , from the wall-to-wall funk and soul music drowning the soundtrack to the nondescript look of the sets -- even the opening title credit sequence has the echo of vintage 1970s productions . the opening sequence featuring ms . grier wordlessly striding through the lax , funky music blaring away on the speakers , is emblematic of films of that era . the timeframe for the film is in fact 1995 , but the atmosphere of jackie brown is decidedly retro . of course , nothing in the film screams 1970s more than the casting of pam grier and robert forster as the two leads , and although the caper intrigue is fun to watch as the plot twists , backstabbing , and deceptions deliciously unfold , the strength of jackie brown is the quiet , understated relationship developed between jackie and max ; when they kiss , it\'s perhaps the most tender scene of the year . tenderness ? in a quentin tarantino film ? sure , there\'ve been moments of sweetness in his prior films -- the affectionate exchanges between the bruce willis and maria de madeiros characters in pulp fiction and the unflagging dedication shared by the characters of tim roth and amanda plummer , or even in reservoir dogs , where a deep , unspoken bond develops between the harvey keitel and tim roth characters -- but for the most part , mr . tarantino\'s films are typified by manic energy , unexpected outbursts of violence , and clever , often wordy , banter . these staples of his work are all present in jackie brown , but what\'s new here is a different facet of his storytelling -- a willingness to imbue the film with a poignant emotional undercurrent , and a patience to draw out several scenes with great deliberation . this effective demonstration of range prohibits the pigeonholing of mr . tarantino as simply a helmer of slick , hip crime dramas with fast-talking lowlifes , and heralds him as a bonafide multifaceted talent ; he\'s the real deal . this new aspect of mr . tarantino\'s storytelling is probably best embodied in a single character -- that of the world-weary , sensitive , and exceedingly-professional max cherry , whose unspoken attraction to jackie is touching . mr . forster\'s nuanced , understated performance is the best in the film ; he creates an amiable character of such poignancy that when he gazes at jackie , we smile along with him . much press has been given about the casting of blaxploitation-era icon pam grier in the lead , with the wags buzzing that mr . tarantino may do for her what his pulp fiction did to bolster john travolta\'s then-sagging career . as it turns out , ms . grier is solid in the film\'s title role , although nothing here forces her to test her range . i do have to take exception to the claim that this film marks her career resurrection , though -- she\'s been working steadily over the years , often in direct-to-video action flicks , but also in such recent theatrical releases as tim burton\'s mars attacks ! and larry cohen\'s original gangstas ( where she first teamed up with mr . forster . ) of course , it\'s true that her role here was a godsend -- a meaty a part as this is rarity for * any * actress , let alone one of her age and current status in the industry . while jackie brown may disappoint those looking for another pulp fiction clone , it marks tremendous growth of mr . tarantino as a director whose horizons are rapidly expanding , and whose characterizations have never been better . and while the film\'s narrative doesn\'t really warrant a running time of 155 minutes , it\'s filled with such sumptuous riches , ranging from the brashness of the vivid soundtrack to entertaining , inconsequential conversations between the characters , that there wasn\'t an unengaging moment . with an impressive trio of feature films under his belt , it\'ll be interesting to see what he tries next . \r\n"; // create an empty Annotation just with the given text Annotation document = new Annotation(text); // run all Annotators on this text pipeline.annotate(document);/*ww w .j a va 2 s .co m*/ System.out.println(text); System.out.println(document.get(TextAnnotation.class)); // these are all the sentences in this document // a CoreMap is essentially a Map that uses class objects as keys and // has values with custom types List<CoreMap> sentences = document.get(SentencesAnnotation.class); StringBuilder lemmas = new StringBuilder(); for (CoreMap sentence : sentences) { // traversing the words in the current sentence // a CoreLabel is a CoreMap with additional token-specific methods //System.out.println("sentence: "+sentence.get(SentenceBeginAnnotation.class)); for (CoreLabel token : sentence.get(TokensAnnotation.class)) { String word = token.get(TextAnnotation.class); //System.out.println("this is the text of the token: "+word); // String pos = token.get(LemmaAnnotation.class); // String ne = token.get(NamedEntityTagAnnotation.class); //System.out.println("this is the NER label of the token: "+ne); if ("O".equals(ne)) lemmas.append(pos).append(" "); } //System.out.println("sentence: "+sentence.get(SentenceEndAnnotation.class)); } System.out.println("this is the lemma tag of the token: " + lemmas); }
From source file:Anaphora_Resolution.Coref.java
/** * @param args the command line arguments *///w w w . j a va 2s.co m public static void main(String[] args) throws IOException, ClassNotFoundException { // creates a StanfordCoreNLP object, with POS tagging, lemmatization, NER, parsing, and coreference resolution Properties props = new Properties(); props.put("annotators", "tokenize, ssplit, pos, lemma, ner, parse, dcoref"); props.put("pos.model", "H:\\nlp jar files\\stanford-postagger-2014-08-27\\stanford-postagger-2014-08-27\\models\\english-left3words-distsim.tagger"); //props.put("dcoref.big.gender.number", "edu/stanford/nlp/models/dcoref/gender.data.gz"); StanfordCoreNLP pipeline = new StanfordCoreNLP(props); // read some text in the text variable String text = "Mary has a little lamb. She is very cute."; // Add your text here! // create an empty Annotation just with the given text Annotation document = new Annotation(text); // run all Annotators on this text pipeline.annotate(document); // these are all the sentences in this document // a CoreMap is essentially a Map that uses class objects as keys and has values with custom types List<CoreMap> sentences = document.get(SentencesAnnotation.class); for (CoreMap sentence : sentences) { // traversing the words in the current sentence // a CoreLabel is a CoreMap with additional token-specific methods for (CoreLabel token : sentence.get(TokensAnnotation.class)) { // this is the text of the token String word = token.get(TextAnnotation.class); // this is the POS tag of the token String pos = token.get(PartOfSpeechAnnotation.class); // this is the NER label of the token String ne = token.get(NamedEntityTagAnnotation.class); } // this is the parse tree of the current sentence Tree tree = sentence.get(TreeAnnotation.class); System.out.println(tree); // this is the Stanford dependency graph of the current sentence SemanticGraph dependencies = sentence.get(CollapsedCCProcessedDependenciesAnnotation.class); } // This is the coreference link graph // Each chain stores a set of mentions that link to each other, // along with a method for getting the most representative mention // Both sentence and token offsets start at 1! Map<Integer, CorefChain> graph = document.get(CorefChainAnnotation.class); System.out.println(graph); }