Edison: Feature extraction for NLP, simplified

Mark Sammons, Christos Christodoulopoulos, Parisa Kordjamshidi, Daniel Khashabi, Vivek Srikumar, Paul Vijayakumar, Mazin Bokhari, Xinbo Wu, Dan Roth

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

When designing Natural Language Processing (NLP) applications that use Machine Learning (ML) techniques, feature extraction becomes a significant part of the development effort, whether developing a new application or attempting to reproduce results reported for existing NLP tasks. We present EDISON, a Java library of feature generation functions used in a suite of state-of-the-art NLP tools, based on a set of generic NLP data structures. These feature extractors populate simple data structures encoding the extracted features, which the package can also serialize to an intuitive JSON file format that can be easily mapped to formats used by ML packages. EDISON can also be used programmatically with JVM-based (Java/Scala) NLP software to provide the feature extractor input. The collection of feature extractors is organised hierarchically and a simple search interface is provided. In this paper we include examples that demonstrate the versatility and ease-of-use of the EDISON feature extraction suite to show that this can significantly reduce the time spent by developers on feature extraction design for NLP systems. The library is publicly hosted at https://github.com/IllinoisCogComp/illinois-cogcomp-nlp/, and we hope that other NLP researchers will contribute to the set of feature extractors. In this way, the community can help simplify reproduction of published results and the integration of ideas from diverse sources when developing new and improved NLP applications.

Original languageEnglish (US)
Title of host publicationProceedings of the 10th International Conference on Language Resources and Evaluation, LREC 2016
EditorsNicoletta Calzolari, Khalid Choukri, Helene Mazo, Asuncion Moreno, Thierry Declerck, Sara Goggi, Marko Grobelnik, Jan Odijk, Stelios Piperidis, Bente Maegaard, Joseph Mariani
PublisherEuropean Language Resources Association (ELRA)
Pages4085-4092
Number of pages8
ISBN (Electronic)9782951740891
StatePublished - 2016
Event10th International Conference on Language Resources and Evaluation, LREC 2016 - Portoroz, Slovenia
Duration: May 23 2016May 28 2016

Publication series

NameProceedings of the 10th International Conference on Language Resources and Evaluation, LREC 2016

Other

Other10th International Conference on Language Resources and Evaluation, LREC 2016
CountrySlovenia
CityPortoroz
Period5/23/165/28/16

Keywords

  • Feature extraction
  • Machine Learning
  • NLP
  • Natural language processing tools
  • Reproducibility

ASJC Scopus subject areas

  • Linguistics and Language
  • Library and Information Sciences
  • Language and Linguistics
  • Education

Fingerprint Dive into the research topics of 'Edison: Feature extraction for NLP, simplified'. Together they form a unique fingerprint.

Cite this