pan-webis-de / huerlimann15

Groningen Lightweight Authorship Detection

GLAD: Groningen Lightweight Authorship Detection

Example uses of glad-main.py

In the following examples the following placeholders are used to have shorter commands (this assumes the PAN2014/15 datasets are downloaded):

$trainingDataset: path/to/data/pan15-authorship-verification-training-dataset-english-2015-03-02

$inputDataset: path/to/data/pan14-author-verification-test-corpus2-english-both-2014-04-22

$modelDir: path/to/models/example_model

Train a model

python3 glad-main.py --training $trainingDataset -i $inputDataset --save_model $modelDir

--training Use this data set to train a model

--save_model Store the model to this directory. If the directory already exists, it will write anyways (and update).

Make predictions using an existing model

python3 glad-main.py -i $inputDataset -m $modelDir

-m Load the model; alternative flag: --model

-i make predictions on the $inputDataset; alternative flags: --test --input

Train and test a model

python3 glad-main.py --training $trainingDataset --test $inputDataset

--training Use this data set to train a model

--test make predictions on the $inputDataset; alternative flags: -i --input

python3 glad-main.py --training $trainingDataset --split

--training Use this data set to train a model

--split split on the training data. Default split: 70%. Define your own split like this: --split 0.5

Writing the predictions

python3 glad-main.py -i $inputDataset -m $modelDir -o Out

-o store the answers.txt file to the directory Out; alternative flag: --out

python3 glad-main.py -i $inputDataset -m $modelDir -a path/to/answers.file

-a store the predictions to a file; alternative flag: --answers

Requirements

• Python 3.x
• NLTK
• NumPy
• scikit-learn
• (liac-arff)