Examples with Feature org.dkpro.tc.api.features.Feature used on opensource projects

Example 31 with Feature

use of org.dkpro.tc.api.features.Feature in project dkpro-tc by dkpro.

the class ContextualityMeasureFeatureExtractor method extract.

@Override
public Set<Feature> extract(JCas jcas, TextClassificationTarget aTarget) throws TextClassificationException {
    Set<Feature> featSet = new HashSet<Feature>();
    double total = selectCovered(jcas, POS.class, aTarget).size();
    double noun = selectCovered(jcas, POS_NOUN.class, aTarget).size() / total;
    double adj = selectCovered(jcas, POS_ADJ.class, aTarget).size() / total;
    double prep = selectCovered(jcas, POS_ADP.class, aTarget).size() / total;
    // !includes
    double art = selectCovered(jcas, POS_DET.class, aTarget).size() / total;
    // determiners
    double pro = selectCovered(jcas, POS_PRON.class, aTarget).size() / total;
    double verb = selectCovered(jcas, POS_VERB.class, aTarget).size() / total;
    double adv = selectCovered(jcas, POS_ADV.class, aTarget).size() / total;
    // noun freq + adj.freq. + prepositions freq. + article freq. - pronoun freq. - verb f. -
    // adverb - interjection + 100
    double contextualityMeasure = 0.5 * (noun + adj + prep + art - pro - verb - adv + 100);
    featSet.add(new Feature("NounRate", noun, FeatureType.NUMERIC));
    featSet.add(new Feature("AdjectiveRate", adj, FeatureType.NUMERIC));
    featSet.add(new Feature("PrepositionRate", prep, FeatureType.NUMERIC));
    featSet.add(new Feature("ArticleRate", art, FeatureType.NUMERIC));
    featSet.add(new Feature("PronounRate", pro, FeatureType.NUMERIC));
    featSet.add(new Feature("VerbRate", verb, FeatureType.NUMERIC));
    featSet.add(new Feature("AdverbRate", adv, FeatureType.NUMERIC));
    featSet.add(new Feature(CONTEXTUALITY_MEASURE_FN, contextualityMeasure, FeatureType.NUMERIC));
    return featSet;
}

Example 32 with Feature

use of org.dkpro.tc.api.features.Feature in project dkpro-tc by dkpro.

the class TopicWordsFeatureExtractor method countWordHits.

private List<Feature> countWordHits(String wordListName, List<String> tokens) throws TextClassificationException {
    // word lists are stored in resources folder relative to feature extractor
    String wordListPath = TopicWordsFeatureExtractor.class.getClassLoader().getResource("./" + wordListName).getPath();
    List<String> topicwords = null;
    try {
        topicwords = FileUtils.readLines(new File(wordListPath), "utf-8");
    } catch (IOException e) {
        throw new TextClassificationException(e);
    }
    int wordcount = 0;
    for (String token : tokens) {
        if (topicwords.contains(token)) {
            wordcount++;
        }
    }
    double numTokens = tokens.size();
    // name the feature same as wordlist
    return Arrays.asList(new Feature(prefix + wordListName, numTokens > 0 ? wordcount / numTokens : 0, FeatureType.NUMERIC));
}

Example 33 with Feature

use of org.dkpro.tc.api.features.Feature in project dkpro-tc by dkpro.

the class TopicWordsFeatureExtractor method extract.

@Override
public Set<Feature> extract(JCas jcas, TextClassificationTarget aTarget) throws TextClassificationException {
    if (topicFilePath == null || topicFilePath.isEmpty()) {
        throw new TextClassificationException("Path to word list must be set!");
    }
    List<String> topics = null;
    Set<Feature> features = new HashSet<Feature>();
    List<String> tokens = JCasUtil.toText(JCasUtil.selectCovered(jcas, Token.class, aTarget));
    try {
        topics = FileUtils.readLines(new File(topicFilePath), "utf-8");
        for (String t : topics) {
            features.addAll(countWordHits(t, tokens));
        }
    } catch (IOException e) {
        throw new TextClassificationException(e);
    }
    return features;
}

Example 34 with Feature

use of org.dkpro.tc.api.features.Feature in project dkpro-tc by dkpro.

the class InitialCharacterUpperCase method extract.

@Override
public Set<Feature> extract(JCas jcas, TextClassificationTarget aTarget) throws TextClassificationException {
    String token = aTarget.getCoveredText();
    boolean bool = Character.isUpperCase(token.charAt(0));
    return new Feature(FEATURE_NAME, bool ? 1.0 : 0.0, bool == false, FeatureType.BOOLEAN).asSet();
}

Example 35 with Feature

use of org.dkpro.tc.api.features.Feature in project dkpro-tc by dkpro.

the class QuestionsRatioFeatureExtractor method extract.

@Override
public Set<Feature> extract(JCas jcas, TextClassificationTarget aTarget) throws TextClassificationException {
    int nrOfSentences = JCasUtil.selectCovered(jcas, Sentence.class, aTarget).size();
    String text = aTarget.getCoveredText();
    // don't count multiple question marks as multiple
    Pattern p = Pattern.compile("\\?[^\\?]");
    // questions
    int matches = 0;
    Matcher m = p.matcher(text);
    while (m.find()) {
        matches++;
    }
    double questionRatio = 0.0;
    if (nrOfSentences > 0) {
        questionRatio = (double) matches / nrOfSentences;
    }
    return new Feature(FN_QUESTION_RATIO, questionRatio, FeatureType.NUMERIC).asSet();
}