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Title: Active Learning for NLP Systems

Abstract

This software implements an active learning framework for Natural Language Processing (NLP) systems. It is intended to be applied on scenarios where limited amount of labeled data is available to train a machine learning-based NLP classification system, but a large set of unlabeled documents exist. This software will point, from the set of unlabeled documents, which ones we should label next so that the overall performance of the classifier is improved.

Developers:
 [1];  [1];  [1];  [1];  [1]
  1. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Release Date:
Project Type:
Open Source, Publicly Available Repository
Software Type:
Scientific
Version:
0.1
Licenses:
MIT License
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)

Primary Award/Contract Number:
AC52-07NA27344
Code ID:
33978
Site Accession Number:
999631
Research Org.:
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Country of Origin:
United States

Citation Formats

Goncalves, Andre R, Sales, Ana, Ranganathan, Hiranmayi, Soper, Braden C, Ray, Priyadip, and USDOE National Nuclear Security Administration. Active Learning for NLP Systems. Computer software. https://www.osti.gov//servlets/purl/1595578. Vers. 0.1. USDOE National Nuclear Security Administration (NNSA). 9 Sep. 2019. Web. doi:10.11578/dc.20200128.6.
Goncalves, Andre R, Sales, Ana, Ranganathan, Hiranmayi, Soper, Braden C, Ray, Priyadip, & USDOE National Nuclear Security Administration. (2019, September 9). Active Learning for NLP Systems (Version 0.1) [Computer software]. https://www.osti.gov//servlets/purl/1595578. doi:10.11578/dc.20200128.6.
Goncalves, Andre R, Sales, Ana, Ranganathan, Hiranmayi, Soper, Braden C, Ray, Priyadip, and USDOE National Nuclear Security Administration. Active Learning for NLP Systems. Computer software. Version 0.1. September 9, 2019. https://www.osti.gov//servlets/purl/1595578. doi:10.11578/dc.20200128.6.
@misc{osti_1595578,
title = {Active Learning for NLP Systems, Version 0.1},
author = {Goncalves, Andre R and Sales, Ana and Ranganathan, Hiranmayi and Soper, Braden C and Ray, Priyadip and USDOE National Nuclear Security Administration},
abstractNote = {This software implements an active learning framework for Natural Language Processing (NLP) systems. It is intended to be applied on scenarios where limited amount of labeled data is available to train a machine learning-based NLP classification system, but a large set of unlabeled documents exist. This software will point, from the set of unlabeled documents, which ones we should label next so that the overall performance of the classifier is improved.},
url = {https://www.osti.gov//servlets/purl/1595578},
doi = {10.11578/dc.20200128.6},
year = {2019},
month = {9},
note =
}

Software:
Publicly Accessible Repository
https://github.com/LLNL/AL_NLP

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