Dump the text of the Gigaword dataset into a single file, for use with language modeling (and other!) toolkits
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Flattening the Gigaword Datset

The scripts in this repository dump the text of the Gigaword dataset into a single file, for use with language modeling (and other!) toolkits.

See my blog post on flattening the Gigaword corpus for more information about how the code in this repo works.

Table of Contents


To run this code, you must have GNU Parallel. This can be installed on Ubuntu with:

sudo apt-get install parallel

This project was developed in Python 3.6, but should work with Python 3.x and 2.x. Please raise an issue if you find that this is not the case.

Conda will set up a virtual environment with the exact version of Python used for development along with all the dependencies needed to run the code in this package.

  1. Download and install conda.

  2. Create a conda environment with Python 3.6.

    conda create -n flat python=3.6
  3. Now activate the conda environment.

    source activate flat
  4. Install the required dependencies with pip.

    pip install -r requirements.txt
  5. Install the required SpaCy data pack.

    python -m spacy download en


flatten_one_gigaword.py takes in the path of a Gigaword data file and an output directory to write a flattened version to. The bash script at flatten_all_gigaword.sh is a thin wrapper that feeds the paths of all the Gigaword data files to flatten_one_gigaword.py and combines the final output.

flatten_all_gigaword.sh takes in three positional arguments:

  1. The path to the Gigaword directory, with all of the data files unzipped.

  2. A directory to write the flattened files to and the final combined output. It will be created if it does not exist.

  3. The number of files to process at once.

For example, you can run:

./flatten_all_gigaword.sh ./data/gigaword_eng_5/ tmp/ 24

to extract data (in parallel, processing 24 files at a time) from the Gigaword corpus at ./data/gigaword_eng_5/ and write the flattened files + combined output to tmp/.