Java Span類代碼示例

import opennlp.tools.util.Span; //導入依賴的package包/類
public Intent interpret(String query) {
    String[] tokens = WhitespaceTokenizer.INSTANCE.tokenize(query);
    double[] outcome = categorizer.categorize(tokens);
    logger.debug(categorizer.getAllResults(outcome));

    Intent intent = new Intent(categorizer.getBestCategory(outcome));

    for (NameFinderME nameFinderME : nameFinderMEs) {
        Span[] spans = nameFinderME.find(tokens);
        String[] names = Span.spansToStrings(spans, tokens);
        for (int i = 0; i < spans.length; i++) {
            intent.getEntities().put(spans[i].getType(), names[i]);
        }
    }

    logger.debug(intent.toString());

    return intent;
}
 

import opennlp.tools.util.Span; //導入依賴的package包/類
@Override
public final boolean incrementToken() throws IOException {
 if (sentences == null) {
     fillSentences();
   }
   
   if (tokenOffset >= sentences.length) {
     return false;
   }
   
   Span sentenceSpan = sentences[tokenOffset];
   clearAttributes();
   int start = sentenceSpan.getStart();
   int end   = sentenceSpan.getEnd();
   termAtt.copyBuffer(inputSentence, start, end - start);
   posIncrAtt.setPositionIncrement(1);
   offsetAtt.setOffset(start, end);
   tokenOffset++;
   
   return true;
}
 

import opennlp.tools.util.Span; //導入依賴的package包/類
public void addSpan(Span span, CSList<Character> labels) throws Exception {
    if (span.getStart() == span.getEnd())
    {
        LabelledPosition lpos = this.get(span.getStart());
        if (lpos == null)
        {
            lpos = new LabelledPosition();
            this.put(span.getStart(),lpos);
            lpos.IsSingleSpan = true;
        }
        else
            lpos.IsSingleSpan = false; 
        lpos.IsStart = true;
        lpos.IsEnd = true;
        LabelledPosition.addNewLabels(lpos.StartLabels,labels);
        LabelledPosition.addNewLabels(lpos.EndLabels,labels);
    }
    else
    {
        addStart(span.getStart(),labels);
        addEnd(span.getEnd(),labels);
    } 
}
 

import opennlp.tools.util.Span; //導入依賴的package包/類
public Boolean isGoodAsTopic(String tag) throws Exception {
    String[] tokens = tokenizeSentence(tag);
    String[] postags = posTagTokens(tokens);
    CSList<Parse> phrase = new CSList<Parse>();
    for (int idx = 0;idx < tokens.length;idx++)
    {
        Parse parse = new Parse(tokens[idx], new Span(0,tokens[idx].length() - 1), postags[idx], 1.0, 1);
        phrase.add(parse);
    }
    Boolean goodAsTopic;
    Boolean goodAsTag;
    RefSupport<Boolean> refVar0 = new RefSupport<Boolean>();
    RefSupport<Boolean> refVar1 = new RefSupport<Boolean>();
    isHighValueObject(phrase, refVar0, refVar1);
    goodAsTag = refVar0.getValue();
    goodAsTopic = refVar1.getValue();
    return goodAsTopic;
}
 

import opennlp.tools.util.Span; //導入依賴的package包/類
public void getSentiment(ContentIndex contentindex) throws Exception {
    for (int i = 0;i < contentindex.ParagraphIndexes.length;i++)
    {
        ParagraphIndex pindex = contentindex.ParagraphIndexes[i];
        pindex.SentenceSentiments = new FloatVector[pindex.SentenceCount];
        pindex.IndexedSentences = new String[pindex.SentenceCount];
        pindex.SpanMap = (HashMap<String,Span>[]) new HashMap[pindex.SentenceCount];
        for (int j = 0;j < pindex.SentenceCount;j++)
        {
            // if we do chunking instead of parsing, then use Shallow Accumulation Breaks
            RefSupport<FloatVector> refVar5 = new RefSupport<FloatVector>();
            getSentimentVector(pindex.SentenceParses[j],
            		pindex.SentenceFlags[j],contentindex.ContentParseDepth,refVar5);
            pindex.SentenceSentiments[j] = refVar5.getValue();
            RefSupport<String> refVar6 = new RefSupport<String>();
            RefSupport<HashMap<String,Span>> refVar7 = new RefSupport<HashMap<String,Span>>();
            makeIndexedSentence(pindex.SentenceParses[j],
            		pindex.SentenceFlags[j],pindex.SentenceSentiments[j],refVar6,refVar7);
            pindex.IndexedSentences[j] = refVar6.getValue();
            pindex.SpanMap[j] = refVar7.getValue();
        }
    }
}
 

import opennlp.tools.util.Span; //導入依賴的package包/類
@Override
public List<Concept> getConcepts(JCas jcas) throws AnalysisEngineProcessException {
  List<Token> tokens = TypeUtil.getOrderedTokens(jcas);
  String[] texts = tokens.stream().map(Token::getCoveredText).toArray(String[]::new);
  String[] pos = tokens.stream().map(Token::getPartOfSpeech).toArray(String[]::new);
  List<Span> spans = insertOutsideSpans(chunker.chunkAsSpans(texts, pos));
  return IntStream.rangeClosed(0, spans.size() - type.size())
          .mapToObj(i -> spans.subList(i, i + type.size()))
          .filter(spansSublist -> type
                  .equals(spansSublist.stream().map(Span::getType).collect(toList())))
          .map(spansSublist -> tokens.subList(spansSublist.get(0).getStart(),
                  spansSublist.get(spansSublist.size() - 1).getEnd()))
          .filter(toks -> toks.size() >= minLength)
          .map(toks -> TypeFactory.createConceptMention(jcas, getFirstTokenBegin(toks),
                  getLastTokenEnd(toks)))
          .map(cmention -> TypeFactory.createConcept(jcas, cmention,
                  TypeFactory.createConceptType(jcas, "opennlp:" + String.join("-", type))))
          .collect(toList());
}
 

import opennlp.tools.util.Span; //導入依賴的package包/類
@Override
public void score(List<LinkedSpan> linkedSpans, String docText, Span[] sentenceSpans, EntityLinkerProperties properties,  AdminBoundaryContext additionalContext) {
   //Map<Double, Double> latLongs = new HashMap<Double, Double>();
  List<GazetteerEntry> allGazEntries = new ArrayList<>();

  /**
   * collect all the gaz entry references
   */
  for (LinkedSpan<BaseLink> ls : linkedSpans) {
    for (BaseLink bl : ls.getLinkedEntries()) {
      if (bl instanceof GazetteerEntry) {
        allGazEntries.add((GazetteerEntry) bl);
      }
    }
  }
  /**
   * use the point clustering to score each hit
   */
  Map<String, List<GazetteerEntry>> cluster = CLUSTERER.cluster(allGazEntries, PRECISION);
  CLUSTERER.scoreClusters(cluster);

}
 

import opennlp.tools.util.Span; //導入依賴的package包/類
private void addParsedAsAnnotations(final JCas jCas, final int offset, final Parse parsed) {
	final String type = parsed.getType();

	// Ignore non phrase types
	if (OpenNLPParser.PHRASE_TYPES.contains(type)) {
		// Otherwise add new ParseChunks

		final Span span = parsed.getSpan();
		final PhraseChunk phraseChunk = new PhraseChunk(jCas);
		phraseChunk.setBegin(offset + span.getStart());
		phraseChunk.setEnd(offset + span.getEnd());
		phraseChunk.setChunkType(parsed.getType());

		addToJCasIndex(phraseChunk);
	}

	Arrays.stream(parsed.getChildren()).forEach(p -> addParsedAsAnnotations(jCas, offset, p));

}
 

import opennlp.tools.util.Span; //導入依賴的package包/類
private Parse parseSentence(final Sentence sentence, final Collection<WordToken> tokens) {
	final String text = sentence.getCoveredText();

	final Parse parse = new Parse(text, new Span(0, text.length()), AbstractBottomUpParser.INC_NODE, 1, 0);

	// Add in the POS
	int index = 0;
	for (final WordToken token : tokens) {
		final Span span = new Span(token.getBegin() - sentence.getBegin(), token.getEnd() - sentence.getBegin());

		parse.insert(new Parse(text, span, AbstractBottomUpParser.TOK_NODE, 0, index));
		index++;
	}

	// Parse the sentence
	return parser.parse(parse);
}
 

import opennlp.tools.util.Span; //導入依賴的package包/類
/**
 * Drop overlapping spans.
 *
 * @param spans the spans
 * @return the span[]
 */
public static Span[] dropOverlappingSpans(Span spans[]) {

	List<Span> sortedSpans = new ArrayList<Span>(spans.length);
	Collections.addAll(sortedSpans, spans);
	Collections.sort(sortedSpans);
	Iterator<Span> it = sortedSpans.iterator();
	Span lastSpan = null;
	while (it.hasNext()) {
		Span span = it.next();
		if (span.equals(lastSpan))
			it.remove();

		lastSpan = span;
	}
	return getTaggedSpans(sortedSpans);

}
 

import opennlp.tools.util.Span; //導入依賴的package包/類
/**
 * Single tagging.
 *
 * @param spans the spans
 * @return the span[]
 */
private static Span[] singleTagging(List<Span> spans) {

	Iterator<Span> it = spans.iterator();
	Span lastSpan = null;
	while (it.hasNext()) {
		Span span = it.next();
		if (lastSpan != null) {
			if (lastSpan.intersects(span)) {
				it.remove();
				span = lastSpan;
			}
		}
		lastSpan = span;
	}
	return spans.toArray(new Span[spans.size()]);
}
 

import opennlp.tools.util.Span; //導入依賴的package包/類
/**
 * Gets the named entity.
 *
 * @param tokenSpan the token span
 * @return the named entity
 */
public static NamedEntity[] getNamedEntity(TokenSpan tokenSpan) {
	Span[] spans = tokenSpan.getSpans();
	String[] tokens = tokenSpan.getTokens();

	String[] spanText = Span.spansToStrings(spans, tokens);
	NamedEntity[] namedEntities = new NamedEntity[spans.length];

	for (int i = 0; i < spans.length; i++) {
		NamedEntity entity = new NamedEntity();
		entity.setEntity(spanText[i]);
		entity.setType(spans[i].getType().split("\\|"));
		namedEntities[i] = entity;
	}

	return namedEntities;
}
 

import opennlp.tools.util.Span; //導入依賴的package包/類
public Set<String> find(String content, String field) {
    try {
        if (!nameFinderModels.containsKey(field)) {
            throw new ElasticsearchException("Could not find field [{}], possible values {}", field, nameFinderModels.keySet());
        }
        TokenNameFinderModel finderModel= nameFinderModels.get(field);
        if (threadLocal.get() == null || !threadLocal.get().equals(finderModel)) {
            threadLocal.set(finderModel);
        }

        String[] tokens = SimpleTokenizer.INSTANCE.tokenize(content);
        Span spans[] = new NameFinderME(finderModel).find(tokens);
        String[] names = Span.spansToStrings(spans, tokens);
        return Sets.newHashSet(names);
    } finally {
        threadLocal.remove();
    }
}
 

import opennlp.tools.util.Span; //導入依賴的package包/類
@Test
public void testPersonNER()
  throws Exception
{
  URL modelUrl = Thread.currentThread().getContextClassLoader()
      .getResource("models/en-ner-persons.bin");
  assertThat(modelUrl, is(notNullValue()));

  TokenNameFinderModel model = new TokenNameFinderModel(modelUrl);
  assertThat(model, is(notNullValue()));

  NameFinderME nameFinder = new NameFinderME(model);
  String[] tokens = SimpleTokenizer.INSTANCE
      .tokenize("Mr. John Smith of New York, married Anne Green of London today.");
  assertThat(tokens.length, is(15));

  Span[] spans = nameFinder.find(tokens);
  assertThat(spans.length, is(2));

  String[] names = Span.spansToStrings(spans, tokens);
  assertThat(names.length, is(2));
  assertThat(names[0], is("John Smith"));
  assertThat(names[1], is("Anne Green"));
}
 

import opennlp.tools.util.Span; //導入依賴的package包/類
@Test
public void testLocationNER()
  throws Exception
{
  URL modelUrl = Thread.currentThread().getContextClassLoader()
      .getResource("models/en-ner-locations.bin");
  assertThat(modelUrl, is(notNullValue()));

  TokenNameFinderModel model = new TokenNameFinderModel(modelUrl);
  assertThat(model, is(notNullValue()));

  NameFinderME nameFinder = new NameFinderME(model);
  String[] tokens = SimpleTokenizer.INSTANCE
      .tokenize("Mr. John Smith of New York, married Anne Green of London today.");
  assertThat(tokens.length, is(15));

  Span[] spans = nameFinder.find(tokens);
  assertThat(spans.length, is(2));

  String[] locations = Span.spansToStrings(spans, tokens);
  assertThat(locations.length, is(2));
  assertThat(locations[0], is("New York"));
  assertThat(locations[1], is("London"));
}