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Stanford CoreNLP provides a set of natural language analysis tools which can take raw English language text input and give the base forms of words, their parts of speech, whether they are names of companies, people, etc., normalize dates, times, and numeric quantities, mark up the structure of sentences in terms of phrases and word dependencies, and indicate which noun phrases refer to the same entities. It provides the foundational building blocks for higher level text understanding applications.

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package edu.stanford.nlp.sentiment;

import java.util.List;

import edu.stanford.nlp.ling.Label;
import edu.stanford.nlp.trees.Tree;
import edu.stanford.nlp.trees.TreeTransformer;
import edu.stanford.nlp.util.Generics;

/**
 * This transformer collapses chains of unary nodes so that the top
 * node is the only node left.  The Sentiment model does not handle
 * unary nodes, so this simplifies them to make a binary tree consist
 * entirely of binary nodes and preterminals.  A new tree with new
 * nodes and labels is returned; the original tree is unchanged.
 *
 * @author John Bauer
 */
public class CollapseUnaryTransformer implements TreeTransformer {
  public Tree transformTree(Tree tree) {
    if (tree.isPreTerminal() || tree.isLeaf()) {
      return tree.deepCopy();
    }

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