本文整理汇总了Java中info.ephyra.nlp.SnowballStemmer.create方法的典型用法代码示例。如果您正苦于以下问题:Java SnowballStemmer.create方法的具体用法?Java SnowballStemmer.create怎么用?Java SnowballStemmer.create使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类info.ephyra.nlp.SnowballStemmer
的用法示例。
在下文中一共展示了SnowballStemmer.create方法的2个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Java代码示例。
示例1: main
import info.ephyra.nlp.SnowballStemmer; //导入方法依赖的package包/类
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);
}
}
示例2: main
import info.ephyra.nlp.SnowballStemmer; //导入方法依赖的package包/类
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);
}
}