Example usage for edu.stanford.nlp.util Generics newArrayList

Introduction

In this page you can find the example usage for edu.stanford.nlp.util Generics newArrayList.

Prototype

public static <E> ArrayList<E> newArrayList() 

Source Link

Usage

From source file:CollapseUnaryTransformer.java

License:Apache License

public Tree transformTree(Tree tree) {
    if (tree.isPreTerminal() || tree.isLeaf()) {
        return tree.deepCopy();
    }//  w w  w  . j a va2s.  c om

    Label label = tree.label().labelFactory().newLabel(tree.label());
    Tree[] children = tree.children();
    while (children.length == 1 && !children[0].isLeaf()) {
        children = children[0].children();
    }
    List<Tree> processedChildren = Generics.newArrayList();
    for (Tree child : children) {
        processedChildren.add(transformTree(child));
    }
    return tree.treeFactory().newTreeNode(label, processedChildren);
}

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From source file:opennlp.tools.parse_thicket.opinion_processor.DefaultSentimentProcessor.java

License:Apache License

/**
 * Reads an annotation from the given filename using the requested input.
 *//*from www . ja va 2s.c  o  m*/
public static List<Annotation> getAnnotations(StanfordCoreNLP tokenizer, Input inputFormat, String filename,
        boolean filterUnknown) {
    switch (inputFormat) {
    case TEXT: {
        String text = IOUtils.slurpFileNoExceptions(filename);
        Annotation annotation = new Annotation(text);
        tokenizer.annotate(annotation);
        List<Annotation> annotations = Generics.newArrayList();
        for (CoreMap sentence : annotation.get(CoreAnnotations.SentencesAnnotation.class)) {
            Annotation nextAnnotation = new Annotation(sentence.get(CoreAnnotations.TextAnnotation.class));
            nextAnnotation.set(CoreAnnotations.SentencesAnnotation.class, Collections.singletonList(sentence));
            annotations.add(nextAnnotation);
        }
        return annotations;
    }
    case TREES: {
        List<Tree> trees;
        if (filterUnknown) {
            trees = SentimentUtils.readTreesWithGoldLabels(filename);
            trees = SentimentUtils.filterUnknownRoots(trees);
        } else {
            trees = Generics.newArrayList();
            MemoryTreebank treebank = new MemoryTreebank("utf-8");
            treebank.loadPath(filename, null);
            for (Tree tree : treebank) {
                trees.add(tree);
            }
        }

        List<Annotation> annotations = Generics.newArrayList();
        for (Tree tree : trees) {
            CoreMap sentence = new Annotation(listToString(tree.yield()));
            sentence.set(TreeCoreAnnotations.TreeAnnotation.class, tree);
            List<CoreMap> sentences = Collections.singletonList(sentence);
            Annotation annotation = new Annotation("");
            annotation.set(CoreAnnotations.SentencesAnnotation.class, sentences);
            annotations.add(annotation);
        }
        return annotations;
    }
    default:
        throw new IllegalArgumentException("Unknown format " + inputFormat);
    }
}

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From source file:startupistanbul.kodinghackathon.service.SentimentService.java

/**
 * Analyze a tweet and return its sentiment
 *
 * @param tweet//  w  w  w.  j a v a  2s  . c o  m
 * @return 1 if positive -1 if negative 0 if neutral
 */
public Integer analyzeTweet(String tweet) {
    Annotation annotation = new Annotation(tweet);
    tokenizer.annotate(annotation);

    List<Annotation> annotations = Generics.newArrayList();
    for (CoreMap sentence : annotation.get(CoreAnnotations.SentencesAnnotation.class)) {
        Annotation nextAnnotation = new Annotation(sentence.get(CoreAnnotations.TextAnnotation.class));
        nextAnnotation.set(CoreAnnotations.SentencesAnnotation.class, Collections.singletonList(sentence));
        annotations.add(nextAnnotation);
    }

    //the total sentiment calculated by counting individual sentences in the tweet
    int totalSentiment = 0;

    //annotate all sentences one by one
    for (Annotation ann : annotations) {
        pipeline.annotate(ann);

        for (CoreMap sentence : ann.get(CoreAnnotations.SentencesAnnotation.class)) {
            //System.out.println(sentence);
            //System.out.println(sentence.get(SentimentCoreAnnotations.ClassName.class));

            String sentenceSentiment = sentence.get(SentimentCoreAnnotations.ClassName.class);
            if (sentenceSentiment.equals("Negative") || sentenceSentiment.equals("Very negative")) {
                totalSentiment--;
            } else if (sentenceSentiment.equals("Positive") || sentenceSentiment.equals("Very positive")) {
                totalSentiment++;
            }
        }
    }

    return totalSentiment > 0 ? 1 : totalSentiment < 0 ? -1 : 0;
}

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