This is an implementation of the Chinese Whispers graph clustering algorithm. For an introduction or if you need to reference the algorithm, use this paper: http://wortschatz.uni-leipzig.de/~cbiemann/pub/2006/BiemannTextGraph06.pdf

This project uses the CW algorithm specifically for Word Sense Induction (WSI).

You can compile the code using Maven, and run the WSI algorithm from the command line.

Here's a quickstart guide:

git clone https://github.com/johannessimon/chinese-whispers.git
cd chinese-whispers && mvn package shade:shade
java -cp target/chinese-whispers.jar de.tudarmstadt.lt.wsi.WSI

You may also of course use the CW algorithm directly from your code.

For an example of how to use the WSI algorithm, compile the code as shown above and download example data, like this word similarity graph extracted from a 120-million-lines English news corpus taken from the JoBimText project: http://sourceforge.net/projects/jobimtext/files/data/models/en_news120M_stanford_lemma/LMI_p1000_l200.gz

The data is formatted in ABC format, meaning that each row contains an edge of the graph, and each row contains three columns separated by a whitespace: from, to, and the edge weight.

Then run the WSI algorithm on the data (making sure you assign enough memory to the VM):

java -Xms4G -Xmx4G -cp target/chinese-whispers.jar de.tudarmstadt.lt.wsi.WSI
-in /path/to/LMI_p1000_l200.gz -n 100 -N 100 -out test-output.txt

The output (in our case test-output.txt) is then formatted as follows:

    word <TAB> cluster-id <TAB> cluster-label <TAB> cluster-node1 cluster-node2 ...
    word <TAB> cluster-id <TAB> cluster-label <TAB> cluster-node1 cluster-node2 ...
    ...