There aren’t many tools which allow you to visualise sentences parsed with dependency grammars. Here’s a small tool which generates a PNG of the dependency graph of a given sentence using the Stanford Parser. You can generate the image for Einey’s quote below by following these steps.

1. Click here to download DependenSee.jar.
2. Download the latest version of the Stanford Parser.  I am using version 1.6.9. (For 1.6.7, use DependenSee.1.6.7.jar and for versions <1.6.6, use DependenSeeOld.jar everywhere. )
3. Extract stanford-parser.jar and englishPCFG.ser.gz in the same folder as DependenSee.jar.
4. On the command prompt, run java -cp DependenSee.jar;stanford-parser.jar com.chaoticity.dependensee.Main "Example isn't another way to teach, it is the only way to teach." out.png (If you are on *nix, replace the semicolon by a colon and make sure you have Arial installed. If you have an already parsed dependency output file, replace the sentence by -t input.txt .)
5. Open out.png and admire :)

I have added Part-of-Speech tags and very basic edge overlap management and might add more eye candy later (curved/coloured edges ?). You can link the library in your code as well. An example is given below. Comments and queries are welcome.


import edu.stanford.nlp.trees.*;
import edu.stanford.nlp.parser.lexparser.*;
import com.chaoticity.dependensee.*;
import java.util.Collection;
class Test {
public static void main(String []args) throws Exception {
String text = "A quick brown fox jumped over the lazy dog.";
TreebankLanguagePack tlp = new PennTreebankLanguagePack();
GrammaticalStructureFactory gsf = tlp.grammaticalStructureFactory();
LexicalizedParser lp = new LexicalizedParser("englishPCFG.ser.gz");
lp.setOptionFlags(new String[]{"-maxLength", "500", "-retainTmpSubcategories"});
Tree tree = lp.apply(text);
GrammaticalStructure gs = gsf.newGrammaticalStructure(tree);
Collection tdl = gs.typedDependenciesCCprocessed(true);
Main.writeImage(tree,tdl, "image.png",3);
}
}


For techies: I’ve been working on citation networks lately. You can visualize such a network as a graph. In this graph, the nodes represent publications (papers,articles etc) and the edges represent citations between them. The graph above was produced using the GraphViz. The data is from the ACL Anthology Network which contains publications from the publicly available ACL Anthology.

For non-techies: Oooooo! pretty picture!