ReVerb is a program that automatically identifies and extracts binary relationships from English sentences. ReVerb is designed for Web-scale information extraction, where the target relations cannot be specified in advance and speed is important.

ReVerb takes raw text as input, and outputs (argument1, relation phrase, argument2) triples. For example, given the sentence "Bananas are an excellent source of potassium," ReVerb will extract the triple (bananas, be source of, potassium).

More information is available at the ReVerb homepage:

Quick Start

If you want to run ReVerb on a small amount of text without modifying its source code, we provide an executable jar file that can be run from the command line. Follow these steps to get started:

  1. Download the latest ReVerb jar from

  2. Run java -Xmx512m -jar reverb.jar yourfile.txt.

  3. Run java -Xmx512m -jar reverb.jar -h for more options.


Building ReVerb from source requires Apache Maven ( Run this command to download the required dependencies, compile, and create a single executable jar file.

mvn clean compile assembly:single

The compiled class files will be put in the target/classes directory. The single executable jar file will be written to target/reverb-core-*-jar-with-dependencies.jar where * is replaced with the version number.

Command Line Interface

Once you have built ReVerb, you can run it from the command line.

The command line interface to ReVerb takes plain text or HTML as input, and outputs a tab-separated table of output. Each row in the output represents a single extracted (argument1, relation phrase, argument2) triple, plus metadata. The output has the following columns:

  1. The filename (or stdin if the source is standard input)
  2. The sentence number this extraction came from.
  3. Argument1 words, space separated
  4. Relation phrase words, space separated
  5. Argument2 words, space separated
  6. The start index of argument1 in the sentence. For example, if the value is i, then the first word of argument1 is the i-1th word in the sentence.
  7. The end index of argument1 in the sentence. For example, if the value is j, then the last word of argument1 is the jth word in the sentence.
  8. The start index of relation phrase.
  9. The end index of relation phrase.
  10. The start index of argument2.
  11. The end index of argument2.
  12. The confidence that this extraction is correct. The higher the number, the more trustworthy this extraction is.
  13. The words of the sentence this extraction came from, space-separated.
  14. The part-of-speech tags for the sentence words, space-separated.
  15. The chunk tags for the sentence words, space separated. These represent a shallow parse of the sentence.
  16. A normalized version of arg1. See the BinaryExtractionNormalizer javadoc for details about how the normalization is done.
  17. A normalized version of rel.
  18. A normalized version of arg2.

For example:

$ echo "Bananas are an excellent source of potassium." | 
    ./reverb -q | tr '\t' '\n' | cat -n
 1  stdin
 2  1
 3  Bananas
 4  are an excellent source of
 5  potassium
 6  0
 7  1
 8  1
 9  6
10  6
11  7
12  0.9999999997341693
13  Bananas are an excellent source of potassium .
16  bananas
17  be source of
18  potassium

For a list of options to the command line interface to ReVerb, run reverb -h.


Running ReVerb on small set of files

./reverb file1 file2 file3 ...

Running ReVerb on standard input

./reverb < input

Running ReVerb on HTML files

The --strip-html flag (short version: -s) removes tags from the input before running ReVerb.

./reverb --strip-html myfile.html

Running ReVerb on a list of files

You may have an entire directory structure that you want to run ReVerb on. ReVerb takes approximately 10 seconds to initialize, so it is not efficient to start a new process for each file. To pass ReVerb a list of paths, use the -f switch:

# Run ReVerb on all files under mydir/
find mydir/ -type f | ./reverb -f

Java Interface

To include ReVerb as a library in your own project, please take a look at the example class ReVerbExample in the src/main/java/edu/washington/cs/knowitall/examples directory.

When running code that calls ReVerb, make sure to increase the Java Virtual Machine heap size by passing the argument -Xmx512m to java. ReVerb loads multiple models into memory, and will be significantly slower if the heap size is not large enough.

Using Eclipse

To modify the ReVerb source code in Eclipse, use Apache Maven to create the appropraite project files:

mvn eclipse:eclipse

Then, start Eclipse and navigate to File > Import. Then, under General, select "Existing Projects into Workspace". Then point Eclipse to the main ReVerb directory.

Including ReVerb as a Dependency

If you want to start a new project that depends on ReVerb, first create a new skeleton project using Maven. The following command will ask you to fill in the details of your project name, etc.:

mvn archetype:generate

Next, add a new repository to the project pom.xml file. You can do this by adding the following XML under the <project> element:


Finally, add ReVerb as a dependency. Do this by adding the following XML under the <project> element:


Your final pom.xml file should look something like this:

<project xmlns="" xmlns:xsi=""






You should be able to include ReVerb in your code now. You can try this out by including import edu.washington.cs.knowitall.extractor.ReVerbExtractor in your program.

Retraining the Confidence Function

ReVerb includes a class for training new confidence functions, given a list of labeled examples, called ReVerbClassifierTrainer. Example code for training a new confidence function confFunction is shown below - the non-trivial part is likely to be converting your labeled data to an Iterable<LabeledBinaryExtraction>.

Example Pseudocode:

// Provide your labeled data here
Iterable<LabeledBinaryExtraction> myLabeledData = ??? 
ReVerbClassifierTrainer trainer = 
    new ReVerbClassifierTrainer(myLabeledData);
Logistic classifier = trainer.getClassifier();
ReVerbConfFunction confFunction = new ReVerbConfFunction(classifier);
 // confFunction is ready to use here.
double conf = confFunction.getConf(extraction);

If you already have a list of binary labeled ReVerb extractions, it should be easy to convert them to ChunkedBinaryExtraction objects, and then to LabeledBinaryExtraction objects (see the constructors for these classes). Also note that ReVerb includes a LabeledBinaryExtractionReader and Writer class. You may wish to (re-)serialize your data using LabeledBinaryExtractionWriter - this will put it in the same format as all previous data used to train ReVerb confidence functions, and it will be easy to read in the future with LabeledBinaryExtractionReader.

Help and Contact

For more information, please visit the ReVerb homepage at the University of Washington:


Citing ReVerb

If you use ReVerb in your academic work, please cite ReVerb with the following BibTeX citation:

  author =   {Anthony Fader and Stephen Soderland and Oren Etzioni},
  title =    {Identifying Relations for Open Information Extraction},
  booktitle =    {Proceedings of the Conference of Empirical Methods
                  in Natural Language Processing ({EMNLP} '11)},
  year =     {2011},
  month =    {July 27-31},
  address =  {Edinburgh, Scotland, UK}