本文整理匯總了Java中edu.stanford.nlp.trees.Tree.pennPrint方法的典型用法代碼示例。如果您正苦於以下問題：Java Tree.pennPrint方法的具體用法？Java Tree.pennPrint怎麽用？Java Tree.pennPrint使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類edu.stanford.nlp.trees.Tree的用法示例。
在下文中一共展示了Tree.pennPrint方法的2個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Java代碼示例。
示例1: demoDP
import edu.stanford.nlp.trees.Tree; //導入方法依賴的package包/類
/**
* demoDP demonstrates turning a file into tokens and then parse trees. Note
* that the trees are printed by calling pennPrint on the Tree object. It is
* also possible to pass a PrintWriter to pennPrint if you want to capture
* the output.
*
* file => tokens => parse trees
*/
public static void demoDP(LexicalizedParser lp, String filename) {
// This option shows loading, sentence-segmenting and tokenizing
// a file using DocumentPreprocessor.
TreebankLanguagePack tlp = new PennTreebankLanguagePack();
GrammaticalStructureFactory gsf = tlp.grammaticalStructureFactory();
// You could also create a tokenizer here (as below) and pass it
// to DocumentPreprocessor
for (List<HasWord> sentence : new DocumentPreprocessor(filename)) {
Tree parse = lp.apply(sentence);
parse.pennPrint();
System.out.println();
GrammaticalStructure gs = gsf.newGrammaticalStructure(parse);
Collection tdl = gs.typedDependenciesCCprocessed();
System.out.println(tdl);
System.out.println();
}
}
示例2: demoAPI
import edu.stanford.nlp.trees.Tree; //導入方法依賴的package包/類
/**
* 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.
*
* difference： already tokenized text
*
*
*/
public static 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 = "Hey @Apple, pretty much all your products are amazing. You blow minds every time you launch a new gizmo."
+ " that said, your hold music is crap";
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 = new PennTreebankLanguagePack();
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("penn,typedDependenciesCollapsed");
tp.printTree(parse);
}