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https://github.com/knowitall/reverb

Web-Scale Open Information Extraction
https://github.com/knowitall/reverb

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Web-Scale Open Information Extraction

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README 

        
# ReVerb



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-latest.jar yourfile.txt`.



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



## Building

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-1`th 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 `j`th 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 .

    14  NNS VBP DT JJ NN IN NN .

    15  B-NP B-VP B-NP I-NP I-NP I-NP I-NP O

    16  bananas

    17  be source of

    18  potassium



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



### Examples



#### 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 

appropriate 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 ReVerb as a dependency. To make sure you are using the latest version

of ReVerb, [consult Maven Central](http://search.maven.org/#search%7Cga%7C1%7Creverb).  Do this by adding the following XML under the `` element:



    

      

        edu.washington.cs.knowitall

        reverb-core

        1.4.1

      

    



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



    

      4.0.0

    

      mygroup

      myartifact

      1.0-SNAPSHOT

      jar



      myartifact

      http://maven.apache.org

    

      

        UTF-8

      

    

      

        

          junit

          junit

          3.8.1

          test

        

        

          edu.washington.cs.knowitall

          reverb-core

          1.4.1

        

      

    



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`.



Example Pseudocode:



    // Provide your labeled data here

    Iterable 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: .



## FAQ



1.  How fast is ReVerb?



    You should really benchmark ReVerb yourself, but on my computer (a new computer in 2011) ReVerb processed 5000 high-quality web sentences in 21 s, or 238 sentences per second, in a single thread.  ReVerb is easily parallelizable by processing different sentences concurrently.



## Contributors

* Anthony Fader 

* Michael Schmitz 

* Robert Bart (rbart at cs.washington.edu)

* Janara Christensen 

* Niranjan Balasubramanian 

* Jonathan Berant 



## Citing ReVerb

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

BibTeX citation:



    @inproceedings{ReVerb2011,

      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}

    }