Examples with Token org.apache.stanbol.enhancer.nlp.model.Token used on opensource projects

Example 21 with Token

use of org.apache.stanbol.enhancer.nlp.model.Token in project stanbol by apache.

the class OpenNlpPosTaggingEngine method posTag.

/**
     * POS tags the parsed tokens by using the pos tagger. Annotations are
     * added based on the posModel and already created adhoc tags.
     * @param tokenList
     * @param posTagger
     * @param posModel
     * @param adhocTags
     * @param language
     */
private void posTag(List<Token> tokenList, POSTagger posTagger, TagSet<PosTag> posModel, Map<String, PosTag> adhocTags, String language) {
    String[] tokenTexts = new String[tokenList.size()];
    for (int i = 0; i < tokenList.size(); i++) {
        tokenTexts[i] = tokenList.get(i).getSpan();
    }
    //get the topK POS tags and props and copy it over to the 2dim Arrays
    Sequence[] posSequences = posTagger.topKSequences(tokenTexts);
    //extract the POS tags and props for the current token from the
    //posSequences.
    //NOTE: Sequence includes always POS tags for all Tokens. If
    //      less then posSequences.length are available it adds the
    //      best match for all followings.
    //      We do not want such copies.
    PosTag[] actPos = new PosTag[posSequences.length];
    double[] actProp = new double[posSequences.length];
    for (int i = 0; i < tokenTexts.length; i++) {
        Token token = tokenList.get(i);
        boolean done = false;
        int j = 0;
        while (j < posSequences.length && !done) {
            String p = posSequences[j].getOutcomes().get(i);
            done = j > 0 && p.equals(actPos[0].getTag());
            if (!done) {
                actPos[j] = getPosTag(posModel, adhocTags, p, language);
                actProp[j] = posSequences[j].getProbs()[i];
                j++;
            }
        }
        //create the POS values
        token.addAnnotations(POS_ANNOTATION, Value.values(actPos, actProp, j));
    }
}

Example 22 with Token

use of org.apache.stanbol.enhancer.nlp.model.Token in project stanbol by apache.

the class OpenNlpTokenizerEngine 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 {
    AnalysedText at = initAnalysedText(this, analysedTextFactory, ci);
    String language = getLanguage(this, ci, true);
    Tokenizer tokenizer = getTokenizer(language);
    if (tokenizer == null) {
        log.warn("Tokenizer for language {} is no longer available. " + "This might happen if the model becomes unavailable during enhancement. " + "If this happens more often it might also indicate an bug in the used " + "EnhancementJobManager implementation as the availability is also checked " + "in the canEnhance(..) method of this Enhancement Engine.");
        return;
    }
    //Try to use sentences for tokenizing
    Iterator<? extends Section> sections = at.getSentences();
    if (!sections.hasNext()) {
        //if no sentences are annotated
        sections = Collections.singleton(at).iterator();
    }
    //for all sentences (or the whole Text - if no sentences available)
    while (sections.hasNext()) {
        Section section = sections.next();
        //Tokenize section
        opennlp.tools.util.Span[] tokenSpans = tokenizer.tokenizePos(section.getSpan());
        for (int i = 0; i < tokenSpans.length; i++) {
            Token token = section.addToken(tokenSpans[i].getStart(), tokenSpans[i].getEnd());
            log.trace(" > add {}", token);
        }
    }
}

Example 23 with Token

use of org.apache.stanbol.enhancer.nlp.model.Token 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();
    }
}