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ATR4S

An open-source library for Automatic Term Recognition written in Scala.

To cite ATR4S:

N.Astrakhantsev. ATR4S: Toolkit with State-of-the-art Automatic Terms Recognition Methods in Scala. arXiv preprint arXiv:1611.07804, 2016.

Implemented algorithms

  1. AvgTermFreq
  2. ResidualIDF
  3. TotalTF-IDF
  4. CValue
  5. Basic
  6. ComboBasic
  7. PostRankDC
  8. Relevance
  9. Weirdness
  10. DomainPertinence
  11. NovelTopicModel
  12. LinkProbability
  13. KeyConceptRelatedness
  14. Voting
  15. PU-ATR

Requirements

Libraries

Scala 2.11

Spark 1.5+ (for Voting and PU-ATR)

Emory nlp4j

(Apache OpenNLP is also supported, but preliminary experiments showed that its quality is not better than Emory nlp4j, while it is not thread-safe; if you are going to use OpenNLP, download models from Apache OpenNLP and place them into src/main/resources)

(Stanford CoreNLP is also supported by this helper, which is moved to a separate module licensed by GPL, due to GPL licensing of Stanford CoreNLP).

Data

In order to use some algorithms you need to download auxiliary files and place them into WORKING_DIRECTORY/data directory (note that working directory can be specified in gradle.properties - by default, this is experiments) or specify path in the corresponding configuration/builder class (e.g. Word2VecAdapterConfig of KeyConceptRelatedness).

Namely,

Datasets used in the experiments can be downloaded from Release page.

OS

PU algorithm may or may not work on Windows due to some bugs in Spark (see relevant questions on Stackoverflow, maybe they help you: 1, 2, 3).

Linking

The library is published into Maven central and JCenter. Add the following lines depending on your build system.

Gradle

compile 'ru.ispras:atr4s:1.2.2'

Maven

<dependency>
    <groupId>ru.ispras</groupId>
    <artifactId>atr4s</artifactId>
    <version>1.2.2</version>
</dependency>

SBT

libraryDependencies += "ru.ispras" % "atr4s" % "1.2.2"

Building from Sources

Build library with gradle:

./gradlew jar

Usage

Command line example

./gradlew recognize -Pdataset=acl2 -PtopCount=10 -Pconfig=CValue.conf -Poutput=cvalueterms.txt

Here we recognize top 10 terms from text files stored in acl2 directory (should be subdirectory of WORKING_DIRECTORY) by CValue measure (stored in CValue.conf file) and writes recognized terms with weights in cvalueterms.txt.

Note that if the encoding of input text files differs from UTF-8, then you should specify the correct encoding in the config of NLPPreprocessor (or convert input files, there are many tools for that).

Program API

See ATRConfig class, which is a Configuration/builder for a facade class AutomaticTermsRecognizer.

See AutomaticTermsRecognizer object for example.

Program API (Java)

Usage in Java does not differ significantly, so see the same classes for examples. However, since Java does not support parameters with default values, we provide helper static functions named make() for most classes containing parameters with default values or parameters with Scala collections, see example below.

Also note that there is a special method returning weighted terms as Java Iterable, so that you won't need to convert Scala collections to Java ones.

class ATRExample {
    public static void main(String[] args) {
        String datasetDir = args[0];
        int topCount = args[1];
        ATRConfig atrConfig = new ATRConfig(EmoryNLPPreprocessorConfig.make(),
                TCCConfig.make(),
                new OneFeatureTCWeighterConfig(Weirdness.make()));
        Iterable<WeightedTerm> terms = atrConfig.build().recognizeAsJavaIterable(datasetDir, topCount);
        for (WeightedTerm termAndWeight: terms) {
            System.out.println(termAndWeight);
        }
    }
}

License

Apache License Version 2.0.