use of org.apache.stanbol.enhancer.nlp.model.Sentence in project stanbol by apache.
the class KuromojiNlpEngine method computeEnhancements.
/**
* Compute enhancements for supplied ContentItem. The results of the process
* are expected to be stored in the metadata of the content item.
* <p/>
* The client (usually an {@link org.apache.stanbol.enhancer.servicesapi.EnhancementJobManager}) should take care of
* persistent storage of the enhanced {@link org.apache.stanbol.enhancer.servicesapi.ContentItem}.
* <p/>
* This method creates a new POSContentPart using {@link org.apache.stanbol.enhancer.engines.pos.api.POSTaggerHelper#createContentPart} from a text/plain part and
* stores it as a new part in the content item. The metadata is not changed.
*
* @throws org.apache.stanbol.enhancer.servicesapi.EngineException
* if the underlying process failed to work as
* expected
*/
@Override
public void computeEnhancements(ContentItem ci) throws EngineException {
final AnalysedText at = initAnalysedText(this, analysedTextFactory, ci);
String language = getLanguage(this, ci, false);
if (!("ja".equals(language) || (language != null && language.startsWith("ja-")))) {
throw new IllegalStateException("The detected language is NOT 'ja'! " + "As this is also checked within the #canEnhance(..) method this " + "indicates an Bug in the used EnhancementJobManager implementation. " + "Please report this on the dev@apache.stanbol.org or create an " + "JIRA issue about this.");
}
//start with the Tokenizer
TokenStream tokenStream = tokenizerFactory.create(new CharSequenceReader(at.getText()));
//build the analyzing chain by adding all TokenFilters
for (TokenFilterFactory filterFactory : filterFactories) {
tokenStream = filterFactory.create(tokenStream);
}
//Try to extract sentences based on POS tags ...
int sentStartOffset = -1;
//NER data
List<NerData> nerList = new ArrayList<NerData>();
//the next index where the NerData.context need to be set
int nerSentIndex = 0;
NerData ner = null;
OffsetAttribute offset = null;
try {
//required with Solr 4
tokenStream.reset();
while (tokenStream.incrementToken()) {
offset = tokenStream.addAttribute(OffsetAttribute.class);
Token token = at.addToken(offset.startOffset(), offset.endOffset());
//Get the POS attribute and init the PosTag
PartOfSpeechAttribute posAttr = tokenStream.addAttribute(PartOfSpeechAttribute.class);
PosTag posTag = POS_TAG_SET.getTag(posAttr.getPartOfSpeech());
if (posTag == null) {
posTag = adhocTags.get(posAttr.getPartOfSpeech());
if (posTag == null) {
posTag = new PosTag(posAttr.getPartOfSpeech());
adhocTags.put(posAttr.getPartOfSpeech(), posTag);
log.warn(" ... missing PosTag mapping for {}", posAttr.getPartOfSpeech());
}
}
//Sentence detection by POS tag
if (sentStartOffset < 0) {
//the last token was a sentence ending
sentStartOffset = offset.startOffset();
}
if (posTag.hasPos(Pos.Point)) {
Sentence sent = at.addSentence(sentStartOffset, offset.startOffset());
//add the sentence as context to the NerData instances
while (nerSentIndex < nerList.size()) {
nerList.get(nerSentIndex).context = sent.getSpan();
nerSentIndex++;
}
sentStartOffset = -1;
}
//POS
token.addAnnotation(POS_ANNOTATION, Value.value(posTag));
//NER
NerTag nerTag = NER_TAG_SET.getTag(posAttr.getPartOfSpeech());
if (ner != null && (nerTag == null || !ner.tag.getType().equals(nerTag.getType()))) {
//write NER annotation
Chunk chunk = at.addChunk(ner.start, ner.end);
chunk.addAnnotation(NlpAnnotations.NER_ANNOTATION, Value.value(ner.tag));
//NOTE that the fise:TextAnnotation are written later based on the nerList
//clean up
ner = null;
}
if (nerTag != null) {
if (ner == null) {
ner = new NerData(nerTag, offset.startOffset());
nerList.add(ner);
}
ner.end = offset.endOffset();
}
BaseFormAttribute baseFormAttr = tokenStream.addAttribute(BaseFormAttribute.class);
MorphoFeatures morpho = null;
if (baseFormAttr != null && baseFormAttr.getBaseForm() != null) {
morpho = new MorphoFeatures(baseFormAttr.getBaseForm());
//and add the posTag
morpho.addPos(posTag);
}
InflectionAttribute inflectionAttr = tokenStream.addAttribute(InflectionAttribute.class);
inflectionAttr.getInflectionForm();
inflectionAttr.getInflectionType();
if (morpho != null) {
//if present add the morpho
token.addAnnotation(MORPHO_ANNOTATION, Value.value(morpho));
}
}
//we still need to write the last sentence
Sentence lastSent = null;
if (offset != null && sentStartOffset >= 0 && offset.endOffset() > sentStartOffset) {
lastSent = at.addSentence(sentStartOffset, offset.endOffset());
}
//and set the context off remaining named entities
while (nerSentIndex < nerList.size()) {
if (lastSent != null) {
nerList.get(nerSentIndex).context = lastSent.getSpan();
} else {
//no sentence detected
nerList.get(nerSentIndex).context = at.getSpan();
}
nerSentIndex++;
}
} catch (IOException e) {
throw new EngineException(this, ci, "Exception while reading from " + "AnalyzedText contentpart", e);
} finally {
try {
tokenStream.close();
} catch (IOException e) {
/* ignore */
}
}
//finally write the NER annotations to the metadata of the ContentItem
final Graph metadata = ci.getMetadata();
ci.getLock().writeLock().lock();
try {
Language lang = new Language("ja");
for (NerData nerData : nerList) {
IRI ta = EnhancementEngineHelper.createTextEnhancement(ci, this);
metadata.add(new TripleImpl(ta, ENHANCER_SELECTED_TEXT, new PlainLiteralImpl(at.getSpan().substring(nerData.start, nerData.end), lang)));
metadata.add(new TripleImpl(ta, DC_TYPE, nerData.tag.getType()));
metadata.add(new TripleImpl(ta, ENHANCER_START, lf.createTypedLiteral(nerData.start)));
metadata.add(new TripleImpl(ta, ENHANCER_END, lf.createTypedLiteral(nerData.end)));
metadata.add(new TripleImpl(ta, ENHANCER_SELECTION_CONTEXT, new PlainLiteralImpl(nerData.context, lang)));
}
} finally {
ci.getLock().writeLock().unlock();
}
}
use of org.apache.stanbol.enhancer.nlp.model.Sentence in project stanbol by apache.
the class TestKuromojiNlpEngine method testEngine.
@Test
public void testEngine() throws EngineException {
LiteralFactory lf = LiteralFactory.getInstance();
Assert.assertEquals(EnhancementEngine.ENHANCE_ASYNC, engine.canEnhance(contentItem));
engine.computeEnhancements(contentItem);
//assert the results
Map<IRI, RDFTerm> expected = new HashMap<IRI, RDFTerm>();
expected.put(Properties.DC_CREATOR, lf.createTypedLiteral(engine.getClass().getName()));
expected.put(Properties.ENHANCER_EXTRACTED_FROM, contentItem.getUri());
Assert.assertEquals(16, EnhancementStructureHelper.validateAllTextAnnotations(contentItem.getMetadata(), text, expected));
AnalysedText at = AnalysedTextUtils.getAnalysedText(contentItem);
Assert.assertNotNull(at);
List<Sentence> sentences = AnalysedTextUtils.asList(at.getSentences());
Assert.assertNotNull(sentences);
Assert.assertEquals(7, sentences.size());
//TODO: values in the following arrays are based on the first run of the
// engine. So this is only to detect changes in results. It can not validate
// that the tokenization and NER detections are correct - sorry I do not
// speak Japanese ...
int[] expectedChunks = new int[] { 5, 3, 1, 0, 1, 2, 4 };
int[] expectedTokens = new int[] { 25, 25, 25, 24, 33, 17, 32 };
int sentIndex = 0;
for (Sentence sent : sentences) {
List<Chunk> sentenceNer = AnalysedTextUtils.asList(sent.getChunks());
Assert.assertEquals(expectedChunks[sentIndex], sentenceNer.size());
for (Chunk chunk : sentenceNer) {
Value<NerTag> nerValue = chunk.getAnnotation(NlpAnnotations.NER_ANNOTATION);
Assert.assertNotNull(nerValue);
Assert.assertNotNull(nerValue.value().getType());
}
List<Token> tokens = AnalysedTextUtils.asList(sent.getTokens());
Assert.assertEquals(expectedTokens[sentIndex], tokens.size());
for (Token token : tokens) {
Value<PosTag> posValue = token.getAnnotation(NlpAnnotations.POS_ANNOTATION);
Assert.assertNotNull(posValue);
}
sentIndex++;
}
}
use of org.apache.stanbol.enhancer.nlp.model.Sentence in project stanbol by apache.
the class CorefFeatureSupportTest method initCorefAnnotations.
private static void initCorefAnnotations() {
Sentence sentence1 = at.addSentence(0, sentenceText1.indexOf(".") + 1);
Token obama = sentence1.addToken(0, "Obama".length());
Sentence sentence2 = at.addSentence(sentenceText1.indexOf(".") + 2, sentenceText2.indexOf(".") + 1);
int heStartIdx = sentence2.getSpan().indexOf("He");
Token he = sentence2.addToken(heStartIdx, heStartIdx + "He".length());
Set<Span> obamaMentions = new HashSet<Span>();
obamaMentions.add(he);
obama.addAnnotation(NlpAnnotations.COREF_ANNOTATION, Value.value(new CorefFeature(true, obamaMentions)));
Set<Span> heMentions = new HashSet<Span>();
heMentions.add(obama);
he.addAnnotation(NlpAnnotations.COREF_ANNOTATION, Value.value(new CorefFeature(false, heMentions)));
}
use of org.apache.stanbol.enhancer.nlp.model.Sentence in project stanbol by apache.
the class DependencyRelationSupportTest method initDepTreeAnnotations.
private static void initDepTreeAnnotations() {
Sentence sentence = at.addSentence(0, text.indexOf(".") + 1);
Token obama = sentence.addToken(0, "Obama".length());
int visitedStartIdx = sentence.getSpan().indexOf("visited");
Token visited = sentence.addToken(visitedStartIdx, visitedStartIdx + "visited".length());
int chinaStartIdx = sentence.getSpan().indexOf("China");
Token china = sentence.addToken(chinaStartIdx, chinaStartIdx + "China".length());
GrammaticalRelationTag nSubjGrammRelTag = new GrammaticalRelationTag("nsubj", GrammaticalRelation.NominalSubject);
obama.addAnnotation(NlpAnnotations.DEPENDENCY_ANNOTATION, Value.value(new DependencyRelation(nSubjGrammRelTag, true, visited)));
GrammaticalRelationTag rootGrammRelTag = new GrammaticalRelationTag("root", GrammaticalRelation.Root);
GrammaticalRelationTag dobjGrammRelTag = new GrammaticalRelationTag("dobj", GrammaticalRelation.DirectObject);
visited.addAnnotation(NlpAnnotations.DEPENDENCY_ANNOTATION, Value.value(new DependencyRelation(rootGrammRelTag, true, null)));
visited.addAnnotation(NlpAnnotations.DEPENDENCY_ANNOTATION, Value.value(new DependencyRelation(nSubjGrammRelTag, false, obama)));
visited.addAnnotation(NlpAnnotations.DEPENDENCY_ANNOTATION, Value.value(new DependencyRelation(dobjGrammRelTag, false, china)));
china.addAnnotation(NlpAnnotations.DEPENDENCY_ANNOTATION, Value.value(new DependencyRelation(dobjGrammRelTag, true, visited)));
}
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