RumourEval2019 Baselines for Task A and Task B

Prerequisites

Keras '2.0.8'

Hyperopt '0.1'

Preprocessing (for both tasks)

1. Download th data from competition Codalab.

https://competitions.codalab.org/competitions/19938

2. Download 300d word vectors pre-trained on Google News corpus.

https://code.google.com/archive/p/word2vec/

3. Change filepaths for data and for word embeddings if needed:

in help_prep_functions.py in loadW2vModel() function insert filepath for word embeddings

in preprocessing_tweets.py and preprocessing_reddit.py change filepaths for data if needed.

4. Choose features option:

In prep_pipeline.py on line 98:

def main(data ='RumEval2019', feats = 'SemEvalfeatures')

feats can be either text for avgw2v representation of the tweets or SemEvalfeatures for additional extra features concatenated with avgw2v.

5. Run preprocessing script

python prep_pipeline.py

Running the model

The description of the model architecture can be found in https://www.aclweb.org/anthology/S/S17/S17-2083.pdf The features used in this code are different to the ones used in the paper.

1. In outer_semeval2019.py you can choose the number of trials that the search algorithm performs while searching for the parameter combination.

2. In parameter_search.py you can define search_space.

3. Run the baseline

python outer_semeval2019.py

If you have any questions feel free to contact me E.Kochkina@warwick.ac.uk or other task organisers rumoureval-2019-organizers@googlegroups.com