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Java SnowballStemmer.create方法代碼示例

本文整理匯總了Java中info.ephyra.nlp.SnowballStemmer.create方法的典型用法代碼示例。如果您正苦於以下問題：Java SnowballStemmer.create方法的具體用法？Java SnowballStemmer.create怎麽用？Java SnowballStemmer.create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info.ephyra.nlp.SnowballStemmer的用法示例。


在下文中一共展示了SnowballStemmer.create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: main

import info.ephyra.nlp.SnowballStemmer; //導入方法依賴的package包/類
public static void main(String[] args) {
		TEST_TERM_DOWMLOD = true;
		
		MsgPrinter.enableStatusMsgs(true);
		MsgPrinter.enableErrorMsgs(true);
		
		// create tokenizer
		MsgPrinter.printStatusMsg("Creating tokenizer...");
		if (!OpenNLP.createTokenizer("res/nlp/tokenizer/opennlp/EnglishTok.bin.gz"))
			MsgPrinter.printErrorMsg("Could not create tokenizer.");
//		LingPipe.createTokenizer();
		
//		// create sentence detector
//		MsgPrinter.printStatusMsg("Creating sentence detector...");
//		if (!OpenNLP.createSentenceDetector("res/nlp/sentencedetector/opennlp/EnglishSD.bin.gz"))
//			MsgPrinter.printErrorMsg("Could not create sentence detector.");
//		LingPipe.createSentenceDetector();
		
		// create stemmer
		MsgPrinter.printStatusMsg("Creating stemmer...");
		SnowballStemmer.create();
		
//		// create part of speech tagger
//		MsgPrinter.printStatusMsg("Creating POS tagger...");
//		if (!OpenNLP.createPosTagger("res/nlp/postagger/opennlp/tag.bin.gz",
//									 "res/nlp/postagger/opennlp/tagdict"))
//			MsgPrinter.printErrorMsg("Could not create OpenNLP POS tagger.");
//		if (!StanfordPosTagger.init("res/nlp/postagger/stanford/" +
//				"train-wsj-0-18.holder"))
//			MsgPrinter.printErrorMsg("Could not create Stanford POS tagger.");
		
//		// create chunker
//		MsgPrinter.printStatusMsg("Creating chunker...");
//		if (!OpenNLP.createChunker("res/nlp/phrasechunker/opennlp/" +
//								   "EnglishChunk.bin.gz"))
//			MsgPrinter.printErrorMsg("Could not create chunker.");
		
		// create named entity taggers
		MsgPrinter.printStatusMsg("Creating NE taggers...");
		NETagger.loadListTaggers("res/nlp/netagger/lists/");
		NETagger.loadRegExTaggers("res/nlp/netagger/patterns.lst");
		MsgPrinter.printStatusMsg("  ...loading models");
//		if (!NETagger.loadNameFinders("res/nlp/netagger/opennlp/"))
//			MsgPrinter.printErrorMsg("Could not create OpenNLP NE tagger.");
//		if (!StanfordNeTagger.isInitialized() && !StanfordNeTagger.init())
//			MsgPrinter.printErrorMsg("Could not create Stanford NE tagger.");
		MsgPrinter.printStatusMsg("  ...done");
		
		WikipediaTermImportanceFilter wtif = new WikipediaTermImportanceFilter(NO_NORMALIZATION, NO_NORMALIZATION, false);
		TRECTarget[] targets = TREC13To16Parser.loadTargets(args[0]);
		for (TRECTarget target : targets) {
			String question = target.getTargetDesc();
			
			// query generation
			MsgPrinter.printGeneratingQueries();
			String qn = QuestionNormalizer.normalize(question);
			MsgPrinter.printNormalization(qn);  // print normalized question string
			Logger.logNormalization(qn);  // log normalized question string
			String[] kws = KeywordExtractor.getKeywords(qn);
			AnalyzedQuestion aq = new AnalyzedQuestion(question);
			aq.setKeywords(kws);
			aq.setFactoid(false);
			
			Query[] queries = new BagOfWordsG().generateQueries(aq);
			for (int q = 0; q < queries.length; q++)
				queries[q].setOriginalQueryString(question);
			
			Result[] results = new Result[1];
			results[0] = new Result("This would be the answer", queries[0]);
			wtif.apply(results);
		}
	}
開發者ID:claritylab，項目名稱:lucida，代碼行數:73，代碼來源:WikipediaTermImportanceFilter.java

示例2: main

import info.ephyra.nlp.SnowballStemmer; //導入方法依賴的package包/類
public static void main(String[] args) {
		TEST_TARGET_GENERATION = true;
		
		MsgPrinter.enableStatusMsgs(true);
		MsgPrinter.enableErrorMsgs(true);
		
		// create tokenizer
		MsgPrinter.printStatusMsg("Creating tokenizer...");
		if (!OpenNLP.createTokenizer("res/nlp/tokenizer/opennlp/EnglishTok.bin.gz"))
			MsgPrinter.printErrorMsg("Could not create tokenizer.");
//		LingPipe.createTokenizer();
		
		// create sentence detector
//		MsgPrinter.printStatusMsg("Creating sentence detector...");
//		if (!OpenNLP.createSentenceDetector("res/nlp/sentencedetector/opennlp/EnglishSD.bin.gz"))
//			MsgPrinter.printErrorMsg("Could not create sentence detector.");
//		LingPipe.createSentenceDetector();
		
		// create stemmer
		MsgPrinter.printStatusMsg("Creating stemmer...");
		SnowballStemmer.create();
		
		// create part of speech tagger
		MsgPrinter.printStatusMsg("Creating POS tagger...");
		if (!OpenNLP.createPosTagger("res/nlp/postagger/opennlp/tag.bin.gz",
									 "res/nlp/postagger/opennlp/tagdict"))
			MsgPrinter.printErrorMsg("Could not create OpenNLP POS tagger.");
//		if (!StanfordPosTagger.init("res/nlp/postagger/stanford/" +
//				"train-wsj-0-18.holder"))
//			MsgPrinter.printErrorMsg("Could not create Stanford POS tagger.");
		
		// create chunker
		MsgPrinter.printStatusMsg("Creating chunker...");
		if (!OpenNLP.createChunker("res/nlp/phrasechunker/opennlp/" +
								   "EnglishChunk.bin.gz"))
			MsgPrinter.printErrorMsg("Could not create chunker.");
		
		// create named entity taggers
		MsgPrinter.printStatusMsg("Creating NE taggers...");
		NETagger.loadListTaggers("res/nlp/netagger/lists/");
		NETagger.loadRegExTaggers("res/nlp/netagger/patterns.lst");
		MsgPrinter.printStatusMsg("  ...loading models");
//		if (!NETagger.loadNameFinders("res/nlp/netagger/opennlp/"))
//			MsgPrinter.printErrorMsg("Could not create OpenNLP NE tagger.");
		if (!StanfordNeTagger.isInitialized() && !StanfordNeTagger.init())
			MsgPrinter.printErrorMsg("Could not create Stanford NE tagger.");
		MsgPrinter.printStatusMsg("  ...done");
		
		WebTermImportanceFilter wtif = new TargetGeneratorTest(NO_NORMALIZATION);
		TRECTarget[] targets = TREC13To16Parser.loadTargets(args[0]);
		for (TRECTarget target : targets) {
			String question = target.getTargetDesc();
			
			// query generation
			MsgPrinter.printGeneratingQueries();
			String qn = QuestionNormalizer.normalize(question);
			MsgPrinter.printNormalization(qn);  // print normalized question string
			Logger.logNormalization(qn);  // log normalized question string
			String[] kws = KeywordExtractor.getKeywords(qn);
			AnalyzedQuestion aq = new AnalyzedQuestion(question);
			aq.setKeywords(kws);
			aq.setFactoid(false);
			
			Query[] queries = new BagOfWordsG().generateQueries(aq);
			for (int q = 0; q < queries.length; q++)
				queries[q].setOriginalQueryString(question);
			
			Result[] results = new Result[1];
			results[0] = new Result("This would be the answer", queries[0]);
			wtif.apply(results);
		}
	}
開發者ID:claritylab，項目名稱:lucida，代碼行數:73，代碼來源:WebTermImportanceFilter.java


注：本文中的info.ephyra.nlp.SnowballStemmer.create方法示例由純淨天空整理自Github/MSDocs等開源代碼及文檔管理平台，相關代碼片段篩選自各路編程大神貢獻的開源項目，源碼版權歸原作者所有，傳播和使用請參考對應項目的License；未經允許，請勿轉載。